GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks
| Autor: | Edward Collier, Supratik Mukhopadhyay |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Class (computer programming)
Artificial neural network business.industry Computer science Knowledge engineering 02 engineering and technology 010501 environmental sciences 01 natural sciences Set (abstract data type) Adversarial system Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence business Generative grammar 0105 earth and related environmental sciences |
| Zdroj: | ICPR |
| Popis: | Recent work in deep neural networks has sought to characterize the nature in which a network learns features and how applicable learnt features are to various problem sets. Deep neural network applicability can be split into three sub-problems; set applicability, class applicability, and instance applicability. In this work we seek to quantify the applicability of features learned during adversarial training, focusing specifically on set and class applicability. We apply techniques for measuring applicability to both generators and discriminators trained on various data sets to quantify applicability. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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