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	Author	Sangeeth Reddy; Minesh Mathew; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar
	Title	RoadText-1K: Text Detection and Recognition Dataset for Driving Videos			Type	Conference Article
	Year	2020	Publication	IEEE International Conference on Robotics and Automation	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection, recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/ projects/cvit-projects/roadtext-1k
	Address	Paris; Francia; ???
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICRA
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ RMG2020			Serial	3400
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	Author	Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados
	Title	Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting			Type	Conference Article
	Year	2021	Publication	16th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	12822	Issue		Pages	381–395
	Keywords
	Abstract	In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work.
	Address	Lausanne; Suissa; September 2021
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG; 600.121; 600.140; 110.312			Approved	no
	Call Number	Admin @ si @ RMG2021			Serial	3572
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	Author	Zahra Raisi-Estabragh; Carlos Martin-Isla; Louise Nissen; Liliana Szabo; Victor M. Campello; Sergio Escalera; Simon Winther; Morten Bottcher; Karim Lekadir; and Steffen E. Petersen
	Title	Radiomics analysis enhances the diagnostic performance of CMR stress perfusion: a proof-of-concept study using the Dan-NICAD dataset			Type	Journal Article
	Year	2023	Publication	Frontiers in Cardiovascular Medicine	Abbreviated Journal	FCM
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ RMN2023			Serial	3937
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	Author	Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski
	Title	Kornia: an Open Source Differentiable Computer Vision Library for PyTorch			Type	Conference Article
	Year	2020	Publication	IEEE Winter Conference on Applications of Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Aspen; Colorado; USA; March 2020
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	WACV
	Notes	MSIAU; 600.122; 600.130			Approved	no
	Call Number	Admin @ si @ RMP2020			Serial	3291
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	Author	Mario Rojas; David Masip; A. Todorov; Jordi Vitria
	Title	Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models			Type	Journal Article
	Year	2011	Publication	PloS one	Abbreviated Journal	Plos
	Volume	6	Issue	8	Pages	e23323
	Keywords
	Abstract	JCR Impact Factor 2010: 4.411 Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions
	Address
	Corporate Author				Thesis
	Publisher	Public Library of Science	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ RMT2011			Serial	1883
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	Author	Mario Rojas; David Masip; Jordi Vitria
	Title	Automatic Detection of Facial Feature Points via HOGs and Geometric Prior Models			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	371-378
	Keywords
	Abstract	Most applications dealing with problems involving the face require a robust estimation of the facial salient points. Nevertheless, this estimation is not usually an automated preprocessing step in applications dealing with facial expression recognition. In this paper we present a simple method to detect facial salient points in the face. It is based on a prior Point Distribution Model and a robust object descriptor. The model learns the distribution of the points from the training data, as well as the amount of variation in location each point exhibits. Using this model, we reduce the search areas to look for each point. In addition, we also exploit the global consistency of the points constellation, increasing the detection accuracy. The method was tested on two separate data sets and the results, in some cases, outperform the state of the art.
	Address	Las Palmas de Gran Canaria. Spain
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-21256-7	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ RMV2011a			Serial	1731
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	Author	Mario Rojas; David Masip; Jordi Vitria
	Title	Predicting Dominance Judgements Automatically: A Machine Learning Approach.			Type	Conference Article
	Year	2011	Publication	IEEE International Workshop on Social Behavior Analysis	Abbreviated Journal
	Volume		Issue		Pages	939-944
	Keywords
	Abstract	The amount of multimodal devices that surround us is growing everyday. In this context, human interaction and communication have become a focus of attention and a hot topic of research. A crucial element in human relations is the evaluation of individuals with respect to facial traits, what is called a first impression. Studies based on appearance have suggested that personality can be expressed by appearance and the observer may use such information to form judgments. In the context of rapid facial evaluation, certain personality traits seem to have a more pronounced effect on the relations and perceptions inside groups. The perception of dominance has been shown to be an active part of social roles at different stages of life, and even play a part in mate selection. The aim of this paper is to study to what extent this information is learnable from the point of view of computer science. Specifically we intend to determine if judgments of dominance can be learned by machine learning techniques. We implement two different descriptors in order to assess this. The first is the histogram of oriented gradients (HOG), and the second is a probabilistic appearance descriptor based on the frequencies of grouped binary tests. State of the art classification rules validate the performance of both descriptors, with respect to the prediction task. Experimental results show that machine learning techniques can predict judgments of dominance rather accurately (accuracies up to 90%) and that the HOG descriptor may characterize appropriately the information necessary for such task.
	Address	Santa Barbara, CA
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4244-9140-7	Medium
	Area		Expedition		Conference	SBA
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ RMV2011b			Serial	1760
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	Author	Aneesh Rangnekar; Zachary Mulhollan; Anthony Vodacek; Matthew Hoffman; Angel Sappa; Erik Blasch; Jun Yu; Liwen Zhang; Shenshen Du; Hao Chang; Keda Lu; Zhong Zhang; Fang Gao; Ye Yu; Feng Shuang; Lei Wang; Qiang Ling; Pranjay Shyam; Kuk-Jin Yoon; Kyung-Soo Kim
	Title	Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022			Type	Conference Article
	Year	2022	Publication	IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)	Abbreviated Journal
	Volume		Issue		Pages	390-398
	Keywords	Training; Computer visio; Conferences; Training data; Object detection; Semisupervised learning; Transformers
	Abstract	This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the Computer Vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results.
	Address	New Orleans; USA; June 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	MSIAU; no menciona			Approved	no
	Call Number	Admin @ si @ RMV2022			Serial	3774
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	Author	Javier Rodenas; Bhalaji Nagarajan; Marc Bolaños; Petia Radeva
	Title	Learning Multi-Subset of Classes for Fine-Grained Food Recognition			Type	Conference Article
	Year	2022	Publication	7th International Workshop on Multimedia Assisted Dietary Management	Abbreviated Journal
	Volume		Issue		Pages	17–26
	Keywords
	Abstract	Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we introduce a new method to improve the classification of classes that are more difficult to discriminate based on Multi-Subsets learning. Using a pre-trained network, we organize classes in multiple subsets using a clustering technique. Later, we embed these subsets in a multi-head model structure. This structure has three distinguishable parts. First, we use several shared blocks to learn the generalized representation of the data. Second, we use multiple specialized blocks focusing on specific subsets that are difficult to distinguish. Lastly, we use a fully connected layer to weight the different subsets in an end-to-end manner by combining the neuron outputs. We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets. Our method was successful in learning the confused classes better and we outperformed the state-of-the-art on the three datasets.
	Address	Lisboa; Portugal; October 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	MADiMa
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ RNB2022			Serial	3797
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	Author	Jordi Roca
	Title	Constancy and inconstancy in categorical colour perception			Type	Book Whole
	Year	2012	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	To recognise objects is perhaps the most important task an autonomous system, either biological or artificial needs to perform. In the context of human vision, this is partly achieved by recognizing the colour of surfaces despite changes in the wavelength distribution of the illumination, a property called colour constancy. Correct surface colour recognition may be adequately accomplished by colour category matching without the need to match colours precisely, therefore categorical colour constancy is likely to play an important role for object identification to be successful. The main aim of this work is to study the relationship between colour constancy and categorical colour perception. Previous studies of colour constancy have shown the influence of factors such the spatio-chromatic properties of the background, individual observer's performance, semantics, etc. However there is very little systematic study of these influences. To this end, we developed a new approach to colour constancy which includes both individual observers' categorical perception, the categorical structure of the background, and their interrelations resulting in a more comprehensive characterization of the phenomenon. In our study, we first developed a new method to analyse the categorical structure of 3D colour space, which allowed us to characterize individual categorical colour perception as well as quantify inter-individual variations in terms of shape and centroid location of 3D categorical regions. Second, we developed a new colour constancy paradigm, termed chromatic setting, which allows measuring the precise location of nine categorically-relevant points in colour space under immersive illumination. Additionally, we derived from these measurements a new colour constancy index which takes into account the magnitude and orientation of the chromatic shift, memory effects and the interrelations among colours and a model of colour naming tuned to each observer/adaptation state. Our results lead to the following conclusions: (1) There exists large inter-individual variations in the categorical structure of colour space, and thus colour naming ability varies significantly but this is not well predicted by low-level chromatic discrimination ability; (2) Analysis of the average colour naming space suggested the need for an additional three basic colour terms (turquoise, lilac and lime) for optimal colour communication; (3) Chromatic setting improved the precision of more complex linear colour constancy models and suggested that mechanisms other than cone gain might be best suited to explain colour constancy; (4) The categorical structure of colour space is broadly stable under illuminant changes for categorically balanced backgrounds; (5) Categorical inconstancy exists for categorically unbalanced backgrounds thus indicating that categorical information perceived in the initial stages of adaptation may constrain further categorical perception.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher		Place of Publication		Editor	Maria Vanrell;C. Alejandro Parraga
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ Roc2012			Serial	2893
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	Author	David Roche
	Title	A Statistical Framework for Terminating Evolutionary Algorithms at their Steady State			Type	Book Whole
	Year	2015	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	As any iterative technique, it is a necessary condition a stop criterion for terminating Evolutionary Algorithms (EA). In the case of optimization methods, the algorithm should stop at the time it has reached a steady state so it can not improve results anymore. Assessing the reliability of termination conditions for EAs is of prime importance. A wrong or weak stop criterion can negatively aect both the computational eort and the nal result. In this Thesis, we introduce a statistical framework for assessing whether a termination condition is able to stop EA at its steady state. In one hand a numeric approximation to steady states to detect the point in which EA population has lost its diversity has been presented for EA termination. This approximation has been applied to dierent EA paradigms based on diversity and a selection of functions covering the properties most relevant for EA convergence. Experiments show that our condition works regardless of the search space dimension and function landscape and Dierential Evolution (DE) arises as the best paradigm. On the other hand, we use a regression model in order to determine the requirements ensuring that a measure derived from EA evolving population is related to the distance to the optimum in xspace. Our theoretical framework is analyzed across several benchmark test functions and two standard termination criteria based on function improvement in f-space and EA population x-space distribution for the DE paradigm. Results validate our statistical framework as a powerful tool for determining the capability of a measure for terminating EA and select the x-space distribution as the best-suited for accurately stopping DE in real-world applications.
	Address	July 2015
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Debora Gil;Jesus Giraldo
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM; 600.075			Approved	no
	Call Number	Admin @ si @ Roc2015			Serial	2686
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	Author	Jose Antonio Rodriguez
	Title	Pen-based Interfaces and Recognition: Application to Proofreading Interpretation			Type	Report
	Year	2006	Publication	CVC Technical Report #96	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	CVC (UAB)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	Admin @ si @ Rod2006			Serial	669
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	Author	Jose Antonio Rodriguez
	Title	Statistical frameworks and prior information modeling in handwritten word-spotting			Type	Book Whole
	Year	2009	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Handwritten word-spotting (HWS) is the pattern analysis task that consists in finding keywords in handwritten document images. So far, HWS has been applied mostly to historical documents in order to build search engines for such image collections. This thesis addresses the problem of word-spotting for detecting important keywords in business documents. This is a first step towards the process of automatic routing of correspondence based on content. However, the application of traditional HWS techniques fails for this type of documents. As opposed to historical documents, real business documents present a very high variability in terms of writing styles, spontaneous writing, crossed-out words, spelling mistakes, etc. The main goal of this thesis is the development of pattern recognition techniques that lead to a high-performance HWS system for this challenging type of data. We develop a statistical framework in which word models are expressed in terms of hidden Markov models and the a priori information is encoded in a universal vocabulary of Gaussian codewords. This systems leads to a very robust performance in word-spotting task. We also find that by constraining the word models to the universal vocabulary, the a priori information of the problem of interest can be exploited for developing new contributions. These include a novel writer adaptation method, a system for searching handwritten words by generating typed text images, and a novel model-based similarity between feature vector sequences.
	Address	Barcelona (Spain)
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Gemma Sanchez;Josep Llados;Florent Perronnin
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	Admin @ si @ Rod2009			Serial	1266
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	Author	Pau Rodriguez
	Title	Towards Robust Neural Models for Fine-Grained Image Recognition			Type	Book Whole
	Year	2019	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Fine-grained recognition, i.e. identifying similar subcategories of the same superclass, is central to human activity. Recognizing a friend, finding bacteria in microscopic imagery, or discovering a new kind of galaxy, are just but few examples. However, fine-grained image recognition is still a challenging computer vision task since the differences between two images of the same category can overwhelm the differences between two images of different fine-grained categories. In this regime, where the difference between two categories resides on subtle input changes, excessively invariant CNNs discard those details that help to discriminate between categories and focus on more obvious changes, yielding poor classification performance. On the other hand, CNNs with too much capacity tend to memorize instance-specific details, thus causing overfitting. In this thesis,motivated by the potential impact of automatic fine-grained image recognition, we tackle the previous challenges and demonstrate that proper alignment of the inputs, multiple levels of attention, regularization, and explicitmodeling of the output space, results inmore accurate fine-grained recognitionmodels, that generalize better, and are more robust to intra-class variation. Concretely, we study the different stages of the neural network pipeline: input pre-processing, attention to regions, feature activations, and the label space. In each stage, we address different issues that hinder the recognition performance on various fine-grained tasks, and devise solutions in each chapter: i)We deal with the sensitivity to input alignment on fine-grained human facial motion such as pain. ii) We introduce an attention mechanism to allow CNNs to choose and process in detail the most discriminate regions of the image. iii)We further extend attention mechanisms to act on the network activations, thus allowing them to correct their predictions by looking back at certain regions, at different levels of abstraction. iv) We propose a regularization loss to prevent high-capacity neural networks to memorize instance details by means of almost-identical feature detectors. v)We finally study the advantages of explicitly modeling the output space within the error-correcting framework. As a result, in this thesis we demonstrate that attention and regularization seem promising directions to overcome the problems of fine-grained image recognition, as well as proper treatment of the input and the output space.
	Address	March 2019
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Jordi Gonzalez;Josep M. Gonfaus;Xavier Roca
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-948531-3-5	Medium
	Area		Expedition		Conference
	Notes	ISE; 600.119			Approved	no
	Call Number	Admin @ si @ Rod2019			Serial	3258
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	Author	Adriana Romero; Carlo Gatta
	Title	Do We Really Need All These Neurons?			Type	Conference Article
	Year	2013	Publication	6th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	7887	Issue		Pages	460--467
	Keywords	Retricted Boltzmann Machine; hidden units; unsupervised learning; classification
	Abstract	Restricted Boltzmann Machines (RBMs) are generative neural networks that have received much attention recently. In particular, choosing the appropriate number of hidden units is important as it might hinder their representative power. According to the literature, RBM require numerous hidden units to approximate any distribution properly. In this paper, we present an experiment to determine whether such amount of hidden units is required in a classification context. We then propose an incremental algorithm that trains RBM reusing the previously trained parameters using a trade-off measure to determine the appropriate number of hidden units. Results on the MNIST and OCR letters databases show that using a number of hidden units, which is one order of magnitude smaller than the literature estimate, suffices to achieve similar performance. Moreover, the proposed algorithm allows to estimate the required number of hidden units without the need of training many RBM from scratch.
	Address	Madeira; Portugal; June 2013
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-38627-5	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB; 600.046			Approved	no
	Call Number	Admin @ si @ RoG2013			Serial	2311
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