mlsanityData to 7 Oct 2026

Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners

arXiv 2111.13550 ↗First posted 26 Nov 2021cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Focusing on discriminative zero-shot learning, in this work we introduce a novel mechanism that dynamically augments during training the set of seen classes to produce additional fictitious classes. These fictitious classes diminish the model's tendency to fixate during training on attribute correlations that appear in the training set but will not appear in newly exposed classes. The proposed model is tested within the two formulations of the zero-shot learning framework; namely, generalized zero-shot learning (GZSL) and classical zero-shot learning (CZSL). Our model improves the state-of-the-art performance on the CUB dataset and reaches comparable results on the other common datasets, AWA2 and SUN. We investigate the strengths and weaknesses of our method, including the effects of catastrophic forgetting when training an end-to-end zero-shot model.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 43 references that are in the map. First posted on arXiv 26 Nov 2021.

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32 of 43 references.

References · 32

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
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  10. 2017Feature Generating Networks for Zero-Shot Learningcited by 257
  11. 2017Zero-Shot Learning -- The Good, the Bad and the Uglycited by 254
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20 more
  1. 2016Learning Deep Representations of Fine-grained Visual Descriptionscited by 182
  2. 2017FearNet: Brain-Inspired Model for Incremental Learningcited by 177
  3. 2019f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learningcited by 164
  4. 2020Attribute Prototype Network for Zero-Shot Learningcited by 160
  5. 2016An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wildcited by 146
  6. 2017Generalized Zero-Shot Learning via Synthesized Examplescited by 114
  7. 2018Multi-modal Cycle-consistent Generalized Zero-Shot Learningcited by 87
  8. 2018Data Augmentation using Random Image Cropping and Patching for Deep CNNscited by 87
  9. 2020Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learningcited by 71
  10. 2021Goal-Oriented Gaze Estimation for Zero-Shot Learningcited by 68
  11. 2017Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networkscited by 66
  12. 2019Episode-based Prototype Generating Network for Zero-Shot Learningcited by 66
  13. 2019Transferable Contrastive Network for Generalized Zero-Shot Learningcited by 64
  14. 2020Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learningcited by 64
  15. 2019Semantic-Guided Multi-Attention Localization for Zero-Shot Learningcited by 58
  16. 2020Generalized Zero-Shot Learning Via Over-Complete Distributioncited by 56
  17. 2020Invertible Zero-Shot Recognition Flowscited by 48
  18. 2018Transductive Unbiased Embedding for Zero-Shot Learningcited by 44
  19. 2018Preserving Semantic Relations for Zero-Shot Learningcited by 35
  20. 2020Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognitioncited by 34

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