Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners

• URL: http://arxiv.org/abs/2111.13550v1
• Date: Fri, 26 Nov 2021 15:41:16 GMT
• Title: Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners
• Authors: Mohammed Dabbah, Ran El-yaniv
• Abstract summary: 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.
• Score: 23.854093182195246
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations [97.41375480696972]
  We introduce Z-ICL, a new zero-shot method that closes the gap by constructing pseudo-demonstrations for a given test input. evaluation on nine classification datasets shows that Z-ICL outperforms previous zero-shot methods by a significant margin.
  arXiv  Detail & Related papers  (2022-12-19T21:34:26Z)
• Mitigating Forgetting in Online Continual Learning via Contrasting Semantically Distinct Augmentations [22.289830907729705]
  Online continual learning (OCL) aims to enable model learning from a non-stationary data stream to continuously acquire new knowledge as well as retain the learnt one. Main challenge comes from the "catastrophic forgetting" issue -- the inability to well remember the learnt knowledge while learning the new ones.
  arXiv  Detail & Related papers  (2022-11-10T05:29:43Z)
• Federated Zero-Shot Learning for Visual Recognition [55.65879596326147]
  We propose a novel Federated Zero-Shot Learning FedZSL framework. FedZSL learns a central model from the decentralized data residing on edge devices. The effectiveness and robustness of FedZSL are demonstrated by extensive experiments conducted on three zero-shot benchmark datasets.
  arXiv  Detail & Related papers  (2022-09-05T14:49:34Z)
• How robust are pre-trained models to distribution shift? [82.08946007821184]
  We show how spurious correlations affect the performance of popular self-supervised learning (SSL) and auto-encoder based models (AE) We develop a novel evaluation scheme with the linear head trained on out-of-distribution (OOD) data, to isolate the performance of the pre-trained models from a potential bias of the linear head used for evaluation.
  arXiv  Detail & Related papers  (2022-06-17T16:18:28Z)
• Self-Supervised Class Incremental Learning [51.62542103481908]
  Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they suffer from catastrophic forgetting: the model cannot discern old class data clearly from the new. In this paper, we explore the performance of Self-Supervised representation learning in Class Incremental Learning (SSCIL) for the first time.
  arXiv  Detail & Related papers  (2021-11-18T06:58:19Z)
• Dynamic VAEs with Generative Replay for Continual Zero-shot Learning [1.90365714903665]
  This paper proposes a novel continual zero-shot learning (DVGR-CZSL) model that grows in size with each task and uses generative replay to update itself with previously learned classes to avoid forgetting. We show our method is superior in task sequentially learning with ZSL(Zero-Shot Learning)
  arXiv  Detail & Related papers  (2021-04-26T10:56:43Z)
• Unsupervised Class-Incremental Learning Through Confusion [0.4604003661048266]
  We introduce a novelty detection method that leverages network confusion caused by training incoming data as a new class. We found that incorporating a class-imbalance during this detection method substantially enhances performance.
  arXiv  Detail & Related papers  (2021-04-09T15:58:43Z)
• Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning [82.07273754143547]
  We propose a meta-continual zero-shot learning (MCZSL) approach to generalizing a model to categories unseen during training. By pairing self-gating of attributes and scaled class normalization with meta-learning based training, we are able to outperform state-of-the-art results.
  arXiv  Detail & Related papers  (2021-02-23T18:36:14Z)
• CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition [52.66360172784038]
  We propose a clustering-based model, which considers all training samples at once, instead of optimizing for each instance individually. We call the proposed method CLASTER and observe that it consistently improves over the state-of-the-art in all standard datasets.
  arXiv  Detail & Related papers  (2021-01-18T12:46:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.