Sparse Orthogonal Parameters Tuning for Continual Learning

• URL: http://arxiv.org/abs/2411.02813v1
• Date: Tue, 05 Nov 2024 05:19:09 GMT
• Title: Sparse Orthogonal Parameters Tuning for Continual Learning
• Authors: Kun-Peng Ning, Hai-Jian Ke, Yu-Yang Liu, Jia-Yu Yao, Yong-Hong Tian, Li Yuan,
• Abstract summary: Continual learning methods based on pre-trained models (PTM) have recently gained attention which adapt to successive downstream tasks without catastrophic forgetting. We propose a novel yet effective method called SoTU (Sparse Orthogonal Parameters TUning)
• Score: 34.462967722928724
• License:
• Abstract: Continual learning methods based on pre-trained models (PTM) have recently gained attention which adapt to successive downstream tasks without catastrophic forgetting. These methods typically refrain from updating the pre-trained parameters and instead employ additional adapters, prompts, and classifiers. In this paper, we from a novel perspective investigate the benefit of sparse orthogonal parameters for continual learning. We found that merging sparse orthogonality of models learned from multiple streaming tasks has great potential in addressing catastrophic forgetting. Leveraging this insight, we propose a novel yet effective method called SoTU (Sparse Orthogonal Parameters TUning). We hypothesize that the effectiveness of SoTU lies in the transformation of knowledge learned from multiple domains into the fusion of orthogonal delta parameters. Experimental evaluations on diverse CL benchmarks demonstrate the effectiveness of the proposed approach. Notably, SoTU achieves optimal feature representation for streaming data without necessitating complex classifier designs, making it a Plug-and-Play solution.

Related papers

• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Parameter-Selective Continual Test-Time Adaptation [3.480626767752489]
  Continual Test-Time Adaptation (CTTA) aims to adapt a pretrained model to ever-changing environments during the test time under continuous domain shifts. PSMT method is capable of effectively updating the critical parameters within the MT network under domain shifts.
  arXiv  Detail & Related papers  (2024-07-02T13:18:15Z)
• Parameter-Efficient Fine-Tuning With Adapters [5.948206235442328]
  This research introduces a novel adaptation method utilizing the UniPELT framework as a base. Our method employs adapters, which enable efficient transfer of pretrained models to new tasks with minimal retraining of the base model parameters.
  arXiv  Detail & Related papers  (2024-05-09T01:40:38Z)
• Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer [44.10678347943115]
  Class-incremental learning (CIL) aims to enable models to continuously learn new classes while overcoming catastrophic forgetting. In this paper, we revisit different parameter-efficient tuning (PET) methods within the context of continual learning. We observe that adapter tuning demonstrates superiority over prompt-based methods, even without parameter expansion in each learning session.
  arXiv  Detail & Related papers  (2024-03-29T05:23:12Z)
• Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach [17.678759882763078]
  Fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features is a key challenge. We propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy.
  arXiv  Detail & Related papers  (2024-03-28T00:14:53Z)
• Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation [67.13876021157887]
  Dynamic Tuning (DyT) is a novel approach to improve both parameter and inference efficiency for ViT adaptation. DyT achieves superior performance compared to existing PEFT methods while evoking only 71% of their FLOPs on the VTAB-1K benchmark.
  arXiv  Detail & Related papers  (2024-03-18T14:05:52Z)
• Rethinking Efficient Tuning Methods from a Unified Perspective [34.67645496324432]
  We revisit the design paradigm of PETL and derive a unified framework U-Tuning for parameter-efficient transfer learning. The U-Tuning framework can simultaneously encompass existing methods and derive new approaches for parameter-efficient transfer learning.
  arXiv  Detail & Related papers  (2023-03-01T17:38:03Z)
• Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models [90.24999406296867]
  In contrast with the standard fine-tuning, delta tuning only fine-tunes a small portion of the model parameters while keeping the rest untouched. Recent studies have demonstrated that a series of delta tuning methods with distinct tuned parameter selection could achieve performance on a par with full- parameter fine-tuning.
  arXiv  Detail & Related papers  (2022-03-14T07:56:32Z)
• Towards a Unified View of Parameter-Efficient Transfer Learning [108.94786930869473]
  Fine-tuning large pre-trained language models on downstream tasks has become the de-facto learning paradigm in NLP. Recent work has proposed a variety of parameter-efficient transfer learning methods that only fine-tune a small number of (extra) parameters to attain strong performance. We break down the design of state-of-the-art parameter-efficient transfer learning methods and present a unified framework that establishes connections between them.
  arXiv  Detail & Related papers  (2021-10-08T20:22:26Z)
• Adaptive Gradient Method with Resilience and Momentum [120.83046824742455]
  We propose an Adaptive Gradient Method with Resilience and Momentum (AdaRem) AdaRem adjusts the parameter-wise learning rate according to whether the direction of one parameter changes in the past is aligned with the direction of the current gradient. Our method outperforms previous adaptive learning rate-based algorithms in terms of the training speed and the test error.
  arXiv  Detail & Related papers  (2020-10-21T14:49:00Z)
• AdaS: Adaptive Scheduling of Stochastic Gradients [50.80697760166045]
  We introduce the notions of textit"knowledge gain" and textit"mapping condition" and propose a new algorithm called Adaptive Scheduling (AdaS) Experimentation reveals that, using the derived metrics, AdaS exhibits: (a) faster convergence and superior generalization over existing adaptive learning methods; and (b) lack of dependence on a validation set to determine when to stop training.
  arXiv  Detail & Related papers  (2020-06-11T16:36:31Z)

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.