Rethinking Class-incremental Learning in the Era of Large Pre-trained Models via Test-Time Adaptation
- URL: http://arxiv.org/abs/2310.11482v2
- Date: Thu, 14 Mar 2024 15:10:05 GMT
- Title: Rethinking Class-incremental Learning in the Era of Large Pre-trained Models via Test-Time Adaptation
- Authors: Imad Eddine Marouf, Subhankar Roy, Enzo Tartaglione, Stéphane Lathuilière,
- Abstract summary: Class-incremental learning (CIL) is a challenging task that involves sequentially learning to categorize classes from new tasks.
We propose Test-Time Adaptation for Class-Incremental Learning (TTACIL) that first fine-tunes PTMs using Adapters on the first task.
Our TTACIL does not undergo any forgetting, while benefiting each task with the rich PTM features.
- Score: 20.62749699589017
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Class-incremental learning (CIL) is a challenging task that involves sequentially learning to categorize classes from new tasks without forgetting previously learned information. The advent of large pre-trained models (PTMs) has fast-tracked the progress in CIL due to the highly transferable PTM representations, where tuning a small set of parameters leads to state-of-the-art performance when compared with the traditional CIL methods that are trained from scratch. However, repeated fine-tuning on each task destroys the rich representations of the PTMs and further leads to forgetting previous tasks. To strike a balance between the stability and plasticity of PTMs for CIL, we propose a novel perspective of eliminating training on every new task and instead train PTM only on the first task, and then refine its representation at inference time using test-time adaptation (TTA). Concretely, we propose Test-Time Adaptation for Class-Incremental Learning (TTACIL) that first fine-tunes PTMs using Adapters on the first task, then adjusts Layer Norm parameters of the PTM on each test instance for learning task-specific features, and finally resets them back to the adapted model to preserve stability. As a consequence, our TTACIL does not undergo any forgetting, while benefiting each task with the rich PTM features. Additionally, by design, our TTACIL is robust to common data corruptions. Our method outperforms several state-of-the-art CIL methods when evaluated on multiple CIL benchmarks under both clean and corrupted data. Code is available at: https://github.com/IemProg/TTACIL.
Related papers
- SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models [26.484208658326857]
Continual learning aims to incrementally acquire new concepts in data streams while resisting forgetting previous knowledge.
With the rise of powerful pre-trained models (PTMs), there is a growing interest in training incremental learning systems.
arXiv Detail & Related papers (2024-11-04T15:34:30Z) - SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training [68.7896349660824]
We present an in-depth analysis of the progressive overfitting problem from the lens of Seq FT.
Considering that the overly fast representation learning and the biased classification layer constitute this particular problem, we introduce the advanced Slow Learner with Alignment (S++) framework.
Our approach involves a Slow Learner to selectively reduce the learning rate of backbone parameters, and a Alignment to align the disjoint classification layers in a post-hoc fashion.
arXiv Detail & Related papers (2024-08-15T17:50:07Z) - 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) - FeTT: Continual Class Incremental Learning via Feature Transformation Tuning [19.765229703131876]
Continual learning (CL) aims to extend deep models from static and enclosed environments to dynamic and complex scenarios.
Recent CL models have gradually shifted towards the utilization of pre-trained models with parameter-efficient fine-tuning strategies.
This paper proposes feature transformation tuning (FeTT) model to non-parametrically fine-tune backbone features across all tasks.
arXiv Detail & Related papers (2024-05-20T06:33:50Z) - Towards Robust Continual Learning with Bayesian Adaptive Moment Regularization [51.34904967046097]
Continual learning seeks to overcome the challenge of catastrophic forgetting, where a model forgets previously learnt information.
We introduce a novel prior-based method that better constrains parameter growth, reducing catastrophic forgetting.
Results show that BAdam achieves state-of-the-art performance for prior-based methods on challenging single-headed class-incremental experiments.
arXiv Detail & Related papers (2023-09-15T17:10:51Z) - Learning to Modulate pre-trained Models in RL [22.812215561012874]
Fine-tuning a pre-trained model often suffers from catastrophic forgetting.
Our study shows that with most fine-tuning approaches, the performance on pre-training tasks deteriorates significantly.
We propose a novel method, Learning-to-Modulate (L2M), that avoids the degradation of learned skills by modulating the information flow of the frozen pre-trained model.
arXiv Detail & Related papers (2023-06-26T17:53:05Z) - Improved Test-Time Adaptation for Domain Generalization [48.239665441875374]
Test-time training (TTT) adapts the learned model with test data.
This work addresses two main factors: selecting an appropriate auxiliary TTT task for updating and identifying reliable parameters to update during the test phase.
We introduce additional adaptive parameters for the trained model, and we suggest only updating the adaptive parameters during the test phase.
arXiv Detail & Related papers (2023-04-10T10:12:38Z) - Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need [84.3507610522086]
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting old ones.
Recent pre-training has achieved substantial progress, making vast pre-trained models (PTMs) accessible for CIL.
We argue that the core factors in CIL are adaptivity for model updating and generalizability for knowledge transferring.
arXiv Detail & Related papers (2023-03-13T17:59:02Z) - Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than
In-Context Learning [81.3514358542452]
Few-shot in-context learning (ICL) incurs substantial computational, memory, and storage costs because it involves processing all of the training examples every time a prediction is made.
parameter-efficient fine-tuning offers an alternative paradigm where a small set of parameters are trained to enable a model to perform the new task.
In this paper, we rigorously compare few-shot ICL and parameter-efficient fine-tuning and demonstrate that the latter offers better accuracy as well as dramatically lower computational costs.
arXiv Detail & Related papers (2022-05-11T17:10:41Z)
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.