Enhanced Few-Shot Class-Incremental Learning via Ensemble Models

• URL: http://arxiv.org/abs/2401.07208v2
• Date: Thu, 21 Mar 2024 14:52:55 GMT
• Title: Enhanced Few-Shot Class-Incremental Learning via Ensemble Models
• Authors: Mingli Zhu, Zihao Zhu, Sihong Chen, Chen Chen, Baoyuan Wu,
• Abstract summary: Few-shot class-incremental learning aims to continually fit new classes with limited training data. The main challenges are overfitting the rare new training samples and forgetting old classes. We propose a new ensemble model framework cooperating with data augmentation to boost generalization.
• Score: 34.84881941101568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot class-incremental learning (FSCIL) aims to continually fit new classes with limited training data, while maintaining the performance of previously learned classes. The main challenges are overfitting the rare new training samples and forgetting old classes. While catastrophic forgetting has been extensively studied, the overfitting problem has attracted less attention in FSCIL. To tackle overfitting challenge, we design a new ensemble model framework cooperated with data augmentation to boost generalization. In this way, the enhanced model works as a library storing abundant features to guarantee fast adaptation to downstream tasks. Specifically, the multi-input multi-output ensemble structure is applied with a spatial-aware data augmentation strategy, aiming at diversifying the feature extractor and alleviating overfitting in incremental sessions. Moreover, self-supervised learning is also integrated to further improve the model generalization. Comprehensive experimental results show that the proposed method can indeed mitigate the overfitting problem in FSCIL, and outperform the state-of-the-art methods.

Related papers

• DiffClass: Diffusion-Based Class Incremental Learning [30.514281721324853]
  Class Incremental Learning (CIL) is challenging due to catastrophic forgetting. Recent exemplar-free CIL methods attempt to mitigate catastrophic forgetting by synthesizing previous task data. We propose a novel exemplar-free CIL method to overcome these issues.
  arXiv  Detail & Related papers  (2024-03-08T03:34:18Z)
• Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders [63.28408887247742]
  We study whether training procedures can be improved to yield better generalization capabilities in the resulting models. We recommend a simple recipe for training dense encoders: Train on MSMARCO with parameter-efficient methods, such as LoRA, and opt for using in-batch negatives unless given well-constructed hard negatives.
  arXiv  Detail & Related papers  (2023-11-16T10:42:58Z)
• Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning [10.111587226277647]
  Few-shot class-incremental learning (FSCIL) aims to build machine learning model that can continually learn new concepts from a few data samples. In this paper, we propose a Sample-to-Class (S2C) graph learning method for FSCIL.
  arXiv  Detail & Related papers  (2023-10-31T08:38:14Z)
• Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants [166.916517335816]
  In this paper, we offer a unified solution to the misalignment dilemma in the three tasks. We propose neural collapse terminus that is a fixed structure with the maximal equiangular inter-class separation for the whole label space. Our method holds the neural collapse optimality in an incremental fashion regardless of data imbalance or data scarcity.
  arXiv  Detail & Related papers  (2023-08-03T13:09:59Z)
• A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation [0.196629787330046]
  Class imbalance (CI) in classification problems arises when the number of observations belonging to one class is lower than the other. In this paper, we evaluate data augmentation and ensemble learning methods used to address prominent benchmark CI problems.
  arXiv  Detail & Related papers  (2023-04-06T04:37:10Z)
• Multimodal Parameter-Efficient Few-Shot Class Incremental Learning [1.9220716793379256]
  Few-Shot Class Incremental Learning (FSCIL) is a challenging continual learning task, where limited training examples are available during several learning sessions. To succeed in this task, it is necessary to avoid over-fitting new classes caused by biased distributions in the few-shot training sets. CPE-CLIP significantly improves FSCIL performance compared to state-of-the-art proposals while also drastically reducing the number of learnable parameters and training costs.
  arXiv  Detail & Related papers  (2023-03-08T17:34:15Z)
• 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)
• Class-Incremental Learning with Strong Pre-trained Models [97.84755144148535]
  Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes) We explore an understudied real-world setting of CIL that starts with a strong model pre-trained on a large number of base classes. Our proposed method is robust and generalizes to all analyzed CIL settings.
  arXiv  Detail & Related papers  (2022-04-07T17:58:07Z)
• Revisiting Consistency Regularization for Semi-Supervised Learning [80.28461584135967]
  We propose an improved consistency regularization framework by a simple yet effective technique, FeatDistLoss. Experimental results show that our model defines a new state of the art for various datasets and settings.
  arXiv  Detail & Related papers  (2021-12-10T20:46:13Z)
• 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)
• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction [166.87111665908333]
  We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL) Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
  arXiv  Detail & Related papers  (2020-04-29T02:32:04Z)

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.