Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning

• URL: http://arxiv.org/abs/2102.11856v1
• Date: Tue, 23 Feb 2021 18:36:14 GMT
• Title: Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning
• Authors: Vinay Kumar Verma, Kevin Liang, Nikhil Mehta, Lawrence Carin
• Abstract summary: 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.
• Score: 82.07273754143547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Zero-shot learning (ZSL) has been shown to be a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges still remain. Recently, methods using generative models to combat bias towards classes seen during training have pushed the state of the art of ZSL, but these generative models can be slow or computationally expensive to train. Additionally, while many previous ZSL methods assume a one-time adaptation to unseen classes, in reality, the world is always changing, necessitating a constant adjustment for deployed models. Models unprepared to handle a sequential stream of data are likely to experience catastrophic forgetting. We propose a meta-continual zero-shot learning (MCZSL) approach to address both these issues. In particular, 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 while being able to train our models substantially faster ($>100\times$) than expensive generative-based approaches. We demonstrate this by performing experiments on five standard ZSL datasets (CUB, aPY, AWA1, AWA2 and SUN) in both generalized zero-shot learning and generalized continual zero-shot learning settings.

Related papers

• Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning [46.6282595346048]
  Zero-shot learning (ZSL) is a promising approach to generalizing a model to unseen categories during training. We propose a Meta-learned Attribute self-Interaction Network (MAIN) for continual ZSL. By pairing attribute self-interaction trained using meta-learning with inverse regularization of the attribute encoder, we are able to outperform state-of-the-art results without leveraging the unseen class attributes.
  arXiv  Detail & Related papers  (2023-12-02T16:23:01Z)
• Continual Zero-Shot Learning through Semantically Guided Generative Random Walks [56.65465792750822]
  We address the challenge of continual zero-shot learning where unseen information is not provided during training, by leveraging generative modeling. We propose our learning algorithm that employs a novel semantically guided Generative Random Walk (GRW) loss. Our algorithm achieves state-of-the-art performance on AWA1, AWA2, CUB, and SUN datasets, surpassing existing CZSL methods by 3-7%.
  arXiv  Detail & Related papers  (2023-08-23T18:10:12Z)
• Evolutionary Generalized Zero-Shot Learning [19.278497273850316]
  Attribute-based Zero-Shot Learning (ZSL) has revolutionized the ability of models to recognize new classes not seen during training. We introduce a scaled-down instantiation of this challenge: Evolutionary Generalized Zero-Shot Learning (EGZSL) This setting allows a low-performing zero-shot model to adapt to the test data stream and evolve online.
  arXiv  Detail & Related papers  (2022-11-23T17:51:18Z)
• Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning [37.00463358780726]
  We propose a Neural Network model that learns a latent feature embedding and a Gaussian Process (GP) regression model that predicts latent feature prototypes of unseen classes. Our model is trained efficiently with a simple training strategy that mitigates the impact of class-imbalanced training data.
  arXiv  Detail & Related papers  (2022-10-11T04:57:20Z)
• 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)
• Attribute-Modulated Generative Meta Learning for Zero-Shot Classification [52.64680991682722]
  We present the Attribute-Modulated generAtive meta-model for Zero-shot learning (AMAZ) Our model consists of an attribute-aware modulation network and an attribute-augmented generative network. Our empirical evaluations show that AMAZ improves state-of-the-art methods by 3.8% and 5.1% in ZSL and generalized ZSL settings, respectively.
  arXiv  Detail & Related papers  (2021-04-22T04:16:43Z)
• Task Aligned Generative Meta-learning for Zero-shot Learning [64.16125851588437]
  We propose a Task-aligned Generative Meta-learning model for Zero-shot learning (TGMZ) TGMZ mitigates the potentially biased training and enables meta-ZSL to accommodate real-world datasets containing diverse distributions. Our comparisons with state-of-the-art algorithms show the improvements of 2.1%, 3.0%, 2.5%, and 7.6% achieved by TGMZ on AWA1, AWA2, CUB, and aPY datasets.
  arXiv  Detail & Related papers  (2021-03-03T05:18:36Z)
• Generative Replay-based Continual Zero-Shot Learning [7.909034037183046]
  We develop a generative replay-based continual ZSL (GRCZSL) The proposed method endows traditional ZSL to learn from streaming data and acquire new knowledge without forgetting the previous tasks' experience. The proposed GRZSL method is developed for a single-head setting of continual learning, simulating a real-world problem setting.
  arXiv  Detail & Related papers  (2021-01-22T00:03:34Z)
• 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.