Lifelong Zero-Shot Learning

Author:

Wei Kun1,Deng Cheng1,Yang Xu1

Affiliation:

1. Xidian University

Abstract

Zero-Shot Learning (ZSL) handles the problem that some testing classes never appear in training set. Existing ZSL methods are designed for learning from a fixed training set, which do not have the ability to capture and accumulate the knowledge of multiple training sets, causing them infeasible to many real-world applications. In this paper, we propose a new ZSL setting, named as Lifelong Zero-Shot Learning (LZSL), which aims to accumulate the knowledge during the learning from multiple datasets and recognize unseen classes of all trained datasets. Besides, a novel method is conducted to realize LZSL, which effectively alleviates the Catastrophic Forgetting in the continuous training process. Specifically, considering those datasets containing different semantic embeddings, we utilize Variational Auto-Encoder to obtain unified semantic representations. Then, we leverage selective retraining strategy to preserve the trained weights of previous tasks and avoid negative transfer when fine-tuning the entire model. Finally, knowledge distillation is employed to transfer knowledge from previous training stages to current stage. We also design the LZSL evaluation protocol and the challenging benchmarks. Extensive experiments on these benchmarks indicate that our method tackles LZSL problem effectively, while existing ZSL methods fail.

Publisher

International Joint Conferences on Artificial Intelligence Organization

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Sketch-Based Replay Projection for Continual Learning;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Agree to Disagree: Exploring Partial Semantic Consistency Against Visual Deviation for Compositional Zero-Shot Learning;IEEE Transactions on Cognitive and Developmental Systems;2024-08

3. Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

4. PAMK: Prototype Augmented Multi-Teacher Knowledge Transfer Network for Continual Zero-Shot Learning;IEEE Transactions on Image Processing;2024

5. A survey on few-shot class-incremental learning;Neural Networks;2024-01

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