Dynamic Integration of Task-Specific Adapters for Class Incremental Learning

• URL: http://arxiv.org/abs/2409.14983v1
• Date: Mon, 23 Sep 2024 13:01:33 GMT
• Title: Dynamic Integration of Task-Specific Adapters for Class Incremental Learning
• Authors: Jiashuo Li, Shaokun Wang, Bo Qian, Yuhang He, Xing Wei, Yihong Gong,
• Abstract summary: Non-exemplar class Incremental Learning (NECIL) enables models to continuously acquire new classes without retraining from scratch and storing old task exemplars. We propose a novel framework called Dynamic Integration of task-specific Adapters (DIA), which comprises two key components: Task-Specific Adapter Integration (TSAI) and Patch-Level Model Alignment.
• Score: 31.67570086108542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-exemplar class Incremental Learning (NECIL) enables models to continuously acquire new classes without retraining from scratch and storing old task exemplars, addressing privacy and storage issues. However, the absence of data from earlier tasks exacerbates the challenge of catastrophic forgetting in NECIL. In this paper, we propose a novel framework called Dynamic Integration of task-specific Adapters (DIA), which comprises two key components: Task-Specific Adapter Integration (TSAI) and Patch-Level Model Alignment. TSAI boosts compositionality through a patch-level adapter integration strategy, which provides a more flexible compositional solution while maintaining low computation costs. Patch-Level Model Alignment maintains feature consistency and accurate decision boundaries via two specialized mechanisms: Patch-Level Distillation Loss (PDL) and Patch-Level Feature Reconstruction method (PFR). Specifically, the PDL preserves feature-level consistency between successive models by implementing a distillation loss based on the contributions of patch tokens to new class learning. The PFR facilitates accurate classifier alignment by reconstructing old class features from previous tasks that adapt to new task knowledge. Extensive experiments validate the effectiveness of our DIA, revealing significant improvements on benchmark datasets in the NECIL setting, maintaining an optimal balance between computational complexity and accuracy. The full code implementation will be made publicly available upon the publication of this paper.

Related papers

• UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation [38.331860053615955]
  This paper introduces a novel framework for unified incremental few-shot object detection (iFSOD) and instance segmentation (iFSIS) using the Transformer architecture. Our goal is to create an optimal solution for situations where only a few examples of novel object classes are available.
  arXiv  Detail & Related papers  (2024-11-13T12:29:44Z)
• CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning [101.81127587760831]
  Current fine-tuning methods build adapters widely of the context of downstream task to learn, or the context of important knowledge to maintain. We propose CorDA, a Context-oriented Decomposition Adaptation method that builds learnable task-aware adapters. Our method enables two options, the knowledge-preserved adaptation and the instruction-previewed adaptation.
  arXiv  Detail & Related papers  (2024-06-07T19:10:35Z)
• Adaptive Rentention & Correction for Continual Learning [114.5656325514408]
  A common problem in continual learning is the classification layer's bias towards the most recent task. We name our approach Adaptive Retention & Correction (ARC) ARC achieves an average performance increase of 2.7% and 2.6% on the CIFAR-100 and Imagenet-R datasets.
  arXiv  Detail & Related papers  (2024-05-23T08:43:09Z)
• Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning [65.57123249246358]
  We propose ExpAndable Subspace Ensemble (EASE) for PTM-based CIL. We train a distinct lightweight adapter module for each new task, aiming to create task-specific subspaces. Our prototype complement strategy synthesizes old classes' new features without using any old class instance.
  arXiv  Detail & Related papers  (2024-03-18T17:58:13Z)
• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• Towards Plastic and Stable Exemplar-Free Incremental Learning: A Dual-Learner Framework with Cumulative Parameter Averaging [12.168402195820649]
  We propose a Dual-Learner framework with Cumulative. Averaging (DLCPA) We show that DLCPA outperforms several state-of-the-art exemplar-free baselines in both Task-IL and Class-IL settings.
  arXiv  Detail & Related papers  (2023-10-28T08:48:44Z)
• 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)
• Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification [57.36281142038042]
  We present a new adaptive block called Contextual Squeeze-and-Excitation (CaSE) that adjusts a pretrained neural network on a new task to significantly improve performance. We also present a new training protocol based on Coordinate-Descent called UpperCaSE that exploits meta-trained CaSE blocks and fine-tuning routines for efficient adaptation.
  arXiv  Detail & Related papers  (2022-06-20T15:25:08Z)
• Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks [59.12108527904171]
  A model should recognize new classes and maintain discriminability over old classes. The task of recognizing few-shot new classes without forgetting old classes is called few-shot class-incremental learning (FSCIL) We propose a new paradigm for FSCIL based on meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT)
  arXiv  Detail & Related papers  (2022-03-31T13:46:41Z)
• Subspace Regularizers for Few-Shot Class Incremental Learning [26.372024890126408]
  We present a new family of subspace regularization schemes that encourage weight vectors for new classes to lie close to the subspace spanned by the weights of existing classes. Our results show that simple geometric regularization of class representations offers an effective tool for continual learning.
  arXiv  Detail & Related papers  (2021-10-13T22:19:53Z)

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.