Diffusion Model Meets Non-Exemplar Class-Incremental Learning and Beyond

• URL: http://arxiv.org/abs/2408.02983v1
• Date: Tue, 6 Aug 2024 06:33:24 GMT
• Title: Diffusion Model Meets Non-Exemplar Class-Incremental Learning and Beyond
• Authors: Jichuan Zhang, Yali Li, Xin Liu, Shengjin Wang,
• Abstract summary: Non-exemplar class-incremental learning (NECIL) is to resist catastrophic forgetting without saving old class samples. We propose a simple, yet effective textbfDiffusion-based textbfFeature textbfReplay (textbfDiffFR) method for NECIL.
• Score: 48.51784137032964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-exemplar class-incremental learning (NECIL) is to resist catastrophic forgetting without saving old class samples. Prior methodologies generally employ simple rules to generate features for replaying, suffering from large distribution gap between replayed features and real ones. To address the aforementioned issue, we propose a simple, yet effective \textbf{Diff}usion-based \textbf{F}eature \textbf{R}eplay (\textbf{DiffFR}) method for NECIL. First, to alleviate the limited representational capacity caused by fixing the feature extractor, we employ Siamese-based self-supervised learning for initial generalizable features. Second, we devise diffusion models to generate class-representative features highly similar to real features, which provides an effective way for exemplar-free knowledge memorization. Third, we introduce prototype calibration to direct the diffusion model's focus towards learning the distribution shapes of features, rather than the entire distribution. Extensive experiments on public datasets demonstrate significant performance gains of our DiffFR, outperforming the state-of-the-art NECIL methods by 3.0\% in average. The code will be made publicly available soon.

Related papers

• Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning [13.836798036474143]
  Key challenge in Federated Class Continual Learning is catastrophic forgetting. We propose a novel method of data replay based on diffusion models. Our method significantly outperforms existing baselines.
  arXiv  Detail & Related papers  (2024-09-02T10:07:24Z)
• Adding Conditional Control to Diffusion Models with Reinforcement Learning [59.295203871547336]
  Diffusion models are powerful generative models that allow for precise control over the characteristics of the generated samples. This work presents a novel method based on reinforcement learning (RL) to add additional controls, leveraging an offline dataset.
  arXiv  Detail & Related papers  (2024-06-17T22:00:26Z)
• Exploring Data Efficiency in Zero-Shot Learning with Diffusion Models [38.36200871549062]
  Zero-Shot Learning (ZSL) aims to enable classifiers to identify unseen classes by enhancing data efficiency at the class level. This is achieved by generating image features from pre-defined semantics of unseen classes. In this paper, we demonstrate that limited seen examples generally result in deteriorated performance of generative models. This unified framework incorporates diffusion models to improve data efficiency at both the class and instance levels.
  arXiv  Detail & Related papers  (2024-06-05T04:37:06Z)
• Guided Diffusion from Self-Supervised Diffusion Features [49.78673164423208]
  Guidance serves as a key concept in diffusion models, yet its effectiveness is often limited by the need for extra data annotation or pretraining. We propose a framework to extract guidance from, and specifically for, diffusion models.
  arXiv  Detail & Related papers  (2023-12-14T11:19:11Z)
• FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning [21.088762527081883]
  Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits the rehearsal of data from previous tasks. Recent approaches to incrementally learning the classifier by freezing the feature extractor after the first task have gained much attention. We explore prototypical networks for CIL, which generate new class prototypes using the frozen feature extractor and classify the features based on the Euclidean distance to the prototypes.
  arXiv  Detail & Related papers  (2023-09-25T11:54:33Z)
• RanPAC: Random Projections and Pre-trained Models for Continual Learning [59.07316955610658]
  Continual learning (CL) aims to learn different tasks (such as classification) in a non-stationary data stream without forgetting old ones. We propose a concise and effective approach for CL with pre-trained models.
  arXiv  Detail & Related papers  (2023-07-05T12:49:02Z)
• ProtoDiffusion: Classifier-Free Diffusion Guidance with Prototype Learning [0.0]
  In this work, we incorporate prototype learning into diffusion models to achieve high generation quality faster than the original diffusion model. We observe that our method, called ProtoDiffusion, achieves better performance in the early stages of training compared to the baseline method.
  arXiv  Detail & Related papers  (2023-07-04T21:18:39Z)
• Structural Pruning for Diffusion Models [65.02607075556742]
  We present Diff-Pruning, an efficient compression method tailored for learning lightweight diffusion models from pre-existing ones. Our empirical assessment, undertaken across several datasets highlights two primary benefits of our proposed method.
  arXiv  Detail & Related papers  (2023-05-18T12:38:21Z)
• Prior Guided Feature Enrichment Network for Few-Shot Segmentation [64.91560451900125]
  State-of-the-art semantic segmentation methods require sufficient labeled data to achieve good results. Few-shot segmentation is proposed to tackle this problem by learning a model that quickly adapts to new classes with a few labeled support samples. Theses frameworks still face the challenge of generalization ability reduction on unseen classes due to inappropriate use of high-level semantic information.
  arXiv  Detail & Related papers  (2020-08-04T10:41:32Z)

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.