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Author (up) Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas edit   pdf
url  openurl
  Title Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings Type Conference Article
  Year 2018 Publication International Workshop on Reproducible Research in Pattern Recognition Abbreviated Journal  
  Volume 11455 Issue Pages 71-82  
  Abstract Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits.  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ NDC2018 Serial 3178  
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