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| Author | Yaxing Wang; L. Zhang; Joost Van de Weijer | ||||
| Title | Ensembles of generative adversarial networks | Type | Conference Article | ||
| Year | 2016 | Publication | 30th Annual Conference on Neural Information Processing Systems Worshops | Abbreviated Journal | |
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| Abstract | Ensembles are a popular way to improve results of discriminative CNNs. The
combination of several networks trained starting from different initializations improves results significantly. In this paper we investigate the usage of ensembles of GANs. The specific nature of GANs opens up several new ways to construct ensembles. The first one is based on the fact that in the minimax game which is played to optimize the GAN objective the generator network keeps on changing even after the network can be considered optimal. As such ensembles of GANs can be constructed based on the same network initialization but just taking models which have different amount of iterations. These so-called self ensembles are much faster to train than traditional ensembles. The second method, called cascade GANs, redirects part of the training data which is badly modeled by the first GAN to another GAN. In experiments on the CIFAR10 dataset we show that ensembles of GANs obtain model probability distributions which better model the data distribution. In addition, we show that these improved results can be obtained at little additional computational cost. |
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| Address | Barcelona; Spain; December 2016 | ||||
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| Area | Expedition | Conference  |
NIPSW | ||
| Notes | LAMP; 600.068 | Approved | no | ||
| Call Number | Admin @ si @ WZW2016 | Serial | 2905 | ||
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| Author | Guim Perarnau; Joost Van de Weijer; Bogdan Raducanu; Jose Manuel Alvarez | ||||
| Title | Invertible conditional gans for image editing | Type | Conference Article | ||
| Year | 2016 | Publication | 30th Annual Conference on Neural Information Processing Systems Worshops | Abbreviated Journal | |
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| Abstract | Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.
Additionally, we evaluate the design of cGANs. The combination of an encoder with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real images with deterministic complex modifications. |
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| Address | Barcelona; Spain; December 2016 | ||||
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| Area | Expedition | Conference |
NIPSW | ||
| Notes | LAMP; ADAS; 600.068 | Approved | no | ||
| Call Number | Admin @ si @ PWR2016 | Serial | 2906 | ||
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| Author | Xavier Baro; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Lukasz Romaszko; Lisheng Sun; Sebastien Treguer; Evelyne Viegas | ||||
| Title | Coompetitions in machine learning: case studies | Type | Conference Article | ||
| Year | 2016 | Publication | 30th Annual Conference on Neural Information Processing Systems Worshops | Abbreviated Journal | |
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| Address | Barcelona; Spain; December 2016 | ||||
| Corporate Author | Thesis | ||||
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NIPSW | ||
| Notes | HuPBA | Approved | no | ||
| Call Number | Admin @ si @ BEG2016 | Serial | 2911 | ||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell | ||||
| Title | Portmanteau Vocabularies for Multi-Cue Image Representation | Type | Conference Article | ||
| Year | 2011 | Publication | 25th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | We describe a novel technique for feature combination in the bag-of-words model of image classification. Our approach builds discriminative compound words from primitive cues learned independently from training images. Our main observation is that modeling joint-cue distributions independently is more statistically robust for typical classification problems than attempting to empirically estimate the dependent, joint-cue distribution directly. We use Information theoretic vocabulary compression to find discriminative combinations of cues and the resulting vocabulary of portmanteau words is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. State-of-the-art results on both the Oxford Flower-102 and Caltech-UCSD Bird-200 datasets demonstrate the effectiveness of our technique compared to other, significantly more complex approaches to multi-cue image representation | ||||
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NIPS | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ KWB2011 | Serial | 1865 | ||
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| Author | B. Zhou; Agata Lapedriza; J. Xiao; A. Torralba; A. Oliva | ||||
| Title | Learning Deep Features for Scene Recognition using Places Database | Type | Conference Article | ||
| Year | 2014 | Publication | 28th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 487-495 | ||
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| Abstract | |||||
| Address | Montreal; Canada; December 2014 | ||||
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NIPS | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ ZLX2014 | Serial | 2621 | ||
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| Author | Boris N. Oreshkin; Pau Rodriguez; Alexandre Lacoste | ||||
| Title | TADAM: Task dependent adaptive metric for improved few-shot learning | Type | Conference Article | ||
| Year | 2018 | Publication | 32nd Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100. | ||||
| Address | Montreal; Canada; December 2018 | ||||
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NIPS | ||
| Notes | ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ ORL2018 | Serial | 3140 | ||
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| Author | Abel Gonzalez-Garcia; Joost Van de Weijer; Yoshua Bengio | ||||
| Title | Image-to-image translation for cross-domain disentanglement | Type | Conference Article | ||
| Year | 2018 | Publication | 32nd Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Address | Montreal; Canada; December 2018 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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NIPS | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ GWB2018 | Serial | 3155 | ||
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| Author | Chenshen Wu; Luis Herranz; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu | ||||
| Title | Memory Replay GANs: Learning to Generate New Categories without Forgetting | Type | Conference Article | ||
| Year | 2018 | Publication | 32nd Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 5966-5976 | ||
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| Abstract | Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first show that sequential fine tuning renders the network unable to properly generate images from previous categories (ie forgetting). Addressing this problem, we propose Memory Replay GANs (MeRGANs), a conditional GAN framework that integrates a memory replay generator. We study two methods to prevent forgetting by leveraging these replays, namely joint training with replay and replay alignment. Qualitative and quantitative experimental results in MNIST, SVHN and LSUN datasets show that our memory replay approach can generate competitive images while significantly mitigating the forgetting of previous categories. | ||||
| Address | Montreal; Canada; December 2018 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference |
NIPS | ||
| Notes | LAMP; 600.106; 600.109; 602.200; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WHL2018 | Serial | 3249 | ||
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| Author | Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui | ||||
| Title | Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation | Type | Conference Article | ||
| Year | 2021 | Publication | Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) | Abbreviated Journal | |
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| Abstract | Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might no longer align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors, and propose a self regularization loss to decrease the negative impact of noisy neighbors. Furthermore, to aggregate information with more context, we consider expanded neighborhoods with small affinity values. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets. Code is available in https://github.com/Albert0147/SFDA_neighbors. | ||||
| Address | Online; December 7-10, 2021 | ||||
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NIPS | ||
| Notes | LAMP; 600.147; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3691 | ||
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| Author | Diego Porres | ||||
| Title | Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks | Type | Conference Article | ||
| Year | 2021 | Publication | Machine Learning for Creativity and Design, Neurips Workshop | Abbreviated Journal | |
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| Abstract | Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at this https URL. | ||||
| Address | Virtual; December 2021 | ||||
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NEURIPSW | ||
| Notes | ADAS; 601.365 | Approved | no | ||
| Call Number | Admin @ si @ Por2021 | Serial | 3597 | ||
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| Author | Riccardo Del Chiaro; Bartlomiej Twardowski; Andrew Bagdanov; Joost Van de Weijer | ||||
| Title | Recurrent attention to transient tasks for continual image captioning | Type | Conference Article | ||
| Year | 2020 | Publication | 34th Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little attention has been focused on continual learning of recurrent models applied to problems like image captioning. In this paper we take a systematic look at continual learning of LSTM-based models for image captioning. We propose an attention-based approach that explicitly accommodates the transient nature of vocabularies in continual image captioning tasks -- i.e. that task vocabularies are not disjoint. We call our method Recurrent Attention to Transient Tasks (RATT), and also show how to adapt continual learning approaches based on weight egularization and knowledge distillation to recurrent continual learning problems. We apply our approaches to incremental image captioning problem on two new continual learning benchmarks we define using the MS-COCO and Flickr30 datasets. Our results demonstrate that RATT is able to sequentially learn five captioning tasks while incurring no forgetting of previously learned ones. | ||||
| Address | virtual; December 2020 | ||||
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NEURIPS | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ CTB2020 | Serial | 3484 | ||
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| Author | Yaxing Wang; Lu Yu; Joost Van de Weijer | ||||
| Title | DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs | Type | Conference Article | ||
| Year | 2020 | Publication | 34th Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require large shape changes. We attribute this to the high-resolution bottlenecks which are used by current state-of-the-art image-to-image methods. Therefore, in this work, we propose a novel deep hierarchical Image-to-Image Translation method, called DeepI2I. We learn a model by leveraging hierarchical features: (a) structural information contained in the shallow layers and (b) semantic information extracted from the deep layers. To enable the training of deep I2I models on small datasets, we propose a novel transfer learning method, that transfers knowledge from pre-trained GANs. Specifically, we leverage the discriminator of a pre-trained GANs (i.e. BigGAN or StyleGAN) to initialize both the encoder and the discriminator and the pre-trained generator to initialize the generator of our model. Applying knowledge transfer leads to an alignment problem between the encoder and generator. We introduce an adaptor network to address this. On many-class image-to-image translation on three datasets (Animal faces, Birds, and Foods) we decrease mFID by at least 35% when compared to the state-of-the-art. Furthermore, we qualitatively and quantitatively demonstrate that transfer learning significantly improves the performance of I2I systems, especially for small datasets. Finally, we are the first to perform I2I translations for domains with over 100 classes. | ||||
| Address | virtual; December 2020 | ||||
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NEURIPS | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WYW2020 | Serial | 3485 | ||
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| Author | Zhengying Liu; Zhen Xu; Shangeth Rajaa; Meysam Madadi; Julio C. S. Jacques Junior; Sergio Escalera; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu; Isabelle Guyon | ||||
| Title | Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019 | Type | Conference Article | ||
| Year | 2020 | Publication | Proceedings of Machine Learning Research | Abbreviated Journal | |
| Volume | 123 | Issue | Pages | 242-252 | |
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| Abstract | We present the design and results of recent competitions in Automated Deep Learning (AutoDL). In the AutoDL challenge series 2019, we organized 5 machine learning challenges: AutoCV, AutoCV2, AutoNLP, AutoSpeech and AutoDL. The first 4 challenges concern each a specific application domain, such as computer vision, natural language processing and speech recognition. At the time of March 2020, the last challenge AutoDL is still on-going and we only present its design. Some highlights of this work include: (1) a benchmark suite of baseline AutoML solutions, with emphasis on domains for which Deep Learning methods have had prior success (image, video, text, speech, etc); (2) a novel any-time learning framework, which opens doors for further theoretical consideration; (3) a repository of around 100 datasets (from all above domains) over half of which are released as public datasets to enable research on meta-learning; (4) analyses revealing that winning solutions generalize to new unseen datasets, validating progress towards universal AutoML solution; (5) open-sourcing of the challenge platform, the starting kit, the dataset formatting toolkit, and all winning solutions (All information available at {autodl.chalearn.org}). | ||||
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NEURIPS | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ LXR2020 | Serial | 3500 | ||
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| Author | Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer | ||||
| Title | Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation | Type | Conference Article | ||
| Year | 2022 | Publication | 36th Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | We propose a simple but effective source-free domain adaptation (SFDA) method.
Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency. This objective encourages local neighborhood features in feature space to have similar predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. |
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| Address | Virtual; November 2022 | ||||
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NEURIPS | ||
| Notes | LAMP; 600.147 | Approved | no | ||
| Call Number | Admin @ si @ YWW2022a | Serial | 3792 | ||
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| Author | Dipam Goswami; Yuyang Liu ; Bartlomiej Twardowski; Joost Van de Weijer | ||||
| Title | FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning | Type | Conference Article | ||
| Year | 2023 | Publication | 37th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | Poster | ||||
| Address | New Orleans; USA; December 2023 | ||||
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NEURIPS | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ GLT2023 | Serial | 3934 | ||
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