Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning

• URL: http://arxiv.org/abs/2305.16687v1
• Date: Fri, 26 May 2023 07:17:24 GMT
• Title: Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning
• Authors: In-Ug Yoon, Tae-Min Choi, Young-Min Kim, Jong-Hwan Kim
• Abstract summary: We develop a simple yet powerful learning scheme that integrates effective methods for each core component of the FSCIL network. In feature extractor training, our goal is to obtain balanced generic representations that benefit both current viewable and unseen or past classes. Our method demonstrates outstanding ability for new task learning and preventing forgetting on CUB200, CIFAR100, and miniImagenet datasets.
• Score: 8.411863266518395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot class-incremental learning (FSCIL) presents the primary challenge of balancing underfitting to a new session's task and forgetting the tasks from previous sessions. To address this challenge, we develop a simple yet powerful learning scheme that integrates effective methods for each core component of the FSCIL network, including the feature extractor, base session classifiers, and incremental session classifiers. In feature extractor training, our goal is to obtain balanced generic representations that benefit both current viewable and unseen or past classes. To achieve this, we propose a balanced supervised contrastive loss that effectively balances these two objectives. In terms of classifiers, we analyze and emphasize the importance of unifying initialization methods for both the base and incremental session classifiers. Our method demonstrates outstanding ability for new task learning and preventing forgetting on CUB200, CIFAR100, and miniImagenet datasets, with significant improvements over previous state-of-the-art methods across diverse metrics. We conduct experiments to analyze the significance and rationale behind our approach and visualize the effectiveness of our representations on new tasks. Furthermore, we conduct diverse ablation studies to analyze the effects of each module.

Related papers

• DATA: Decomposed Attention-based Task Adaptation for Rehearsal-Free Continual Learning [22.386864304549285]
  Continual learning (CL) is essential for Large Language Models (LLMs) to adapt to evolving real-world demands. Recent rehearsal-free methods employ model-based and regularization-based strategies to address this issue. We propose a $textbfD$e $textbfA$ttention-based $textbfTask $textbfA$daptation ( DATA) DATA explicitly decouples and learns both task-specific and task-shared knowledge using high-rank and low-rank task adapters.
  arXiv  Detail & Related papers  (2025-02-17T06:35:42Z)
• Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining [55.262510814326035]
  Existing reweighting strategies primarily focus on group-level data importance. We introduce novel algorithms for dynamic, instance-level data reweighting. Our framework allows us to devise reweighting strategies deprioritizing redundant or uninformative data.
  arXiv  Detail & Related papers  (2025-02-10T17:57:15Z)
• Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning [23.757252768668497]
  Compositional Zero-shot Learning (CZSL) aims to identify novel compositions via known attribute-object pairs. The primary challenge in CZSL tasks lies in the significant discrepancies introduced by the complex interaction between the visual primitives of attribute and object. We propose a model-agnostic and Primitive-Based Adversarial training (PBadv) method to deal with this problem.
  arXiv  Detail & Related papers  (2024-06-21T08:18:30Z)
• Improving Forward Compatibility in Class Incremental Learning by Increasing Representation Rank and Feature Richness [3.0620294646308754]
  We introduce an effective-Rank based Feature Richness enhancement (RFR) method, designed for improving forward compatibility. Our results demonstrate the effectiveness of our approach in enhancing novel-task performance while mitigating catastrophic forgetting.
  arXiv  Detail & Related papers  (2024-03-22T11:14:30Z)
• Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learning [40.13416912075668]
  We propose a rehearsal-free CIL approach that learns continually via the synergy between two Complementary Learning Subnetworks. Our method achieves competitive results against state-of-the-art methods, especially in accuracy gain, memory cost, training efficiency, and task-order.
  arXiv  Detail & Related papers  (2023-06-21T01:43:25Z)
• Accelerating exploration and representation learning with offline pre-training [52.6912479800592]
  We show that exploration and representation learning can be improved by separately learning two different models from a single offline dataset. We show that learning a state representation using noise-contrastive estimation and a model of auxiliary reward can significantly improve the sample efficiency on the challenging NetHack benchmark.
  arXiv  Detail & Related papers  (2023-03-31T18:03:30Z)
• Few-Shot Incremental Learning with Continually Evolved Classifiers [46.278573301326276]
  Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points. The difficulty lies in that limited data from new classes not only lead to significant overfitting issues but also exacerbate the notorious catastrophic forgetting problems. We propose a Continually Evolved CIF ( CEC) that employs a graph model to propagate context information between classifiers for adaptation.
  arXiv  Detail & Related papers  (2021-04-07T10:54:51Z)
• Few-shot Action Recognition with Prototype-centered Attentive Learning [88.10852114988829]
  Prototype-centered Attentive Learning (PAL) model composed of two novel components. First, a prototype-centered contrastive learning loss is introduced to complement the conventional query-centered learning objective. Second, PAL integrates a attentive hybrid learning mechanism that can minimize the negative impacts of outliers.
  arXiv  Detail & Related papers  (2021-01-20T11:48:12Z)
• Incremental Embedding Learning via Zero-Shot Translation [65.94349068508863]
  Current state-of-the-art incremental learning methods tackle catastrophic forgetting problem in traditional classification networks. We propose a novel class-incremental method for embedding network, named as zero-shot translation class-incremental method (ZSTCI) In addition, ZSTCI can easily be combined with existing regularization-based incremental learning methods to further improve performance of embedding networks.
  arXiv  Detail & Related papers  (2020-12-31T08:21:37Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)
• 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.