Auxiliary Classifiers Improve Stability and Efficiency in Continual Learning
- URL: http://arxiv.org/abs/2403.07404v2
- Date: Mon, 07 Oct 2024 20:41:47 GMT
- Title: Auxiliary Classifiers Improve Stability and Efficiency in Continual Learning
- Authors: Filip Szatkowski, Fei Yang, Bartłomiej Twardowski, Tomasz Trzciński, Joost van de Weijer,
- Abstract summary: We investigate the stability of intermediate neural network layers during continual learning.
We show auxiliary classifiers (ACs) can leverage this stability to improve performance.
Our findings suggest that ACs offer a promising avenue for enhancing continual learning models.
- Score: 13.309853617922824
- License:
- Abstract: Continual learning is crucial for applications in dynamic environments, where machine learning models must adapt to changing data distributions while retaining knowledge of previous tasks. Despite significant advancements, catastrophic forgetting - where performance on earlier tasks degrades as new information is learned - remains a key challenge. In this work, we investigate the stability of intermediate neural network layers during continual learning and explore how auxiliary classifiers (ACs) can leverage this stability to improve performance. We show that early network layers remain more stable during learning, particularly for older tasks, and that ACs applied to these layers can outperform standard classifiers on past tasks. By integrating ACs into several continual learning algorithms, we demonstrate consistent and significant performance improvements on standard benchmarks. Additionally, we explore dynamic inference, showing that AC-augmented continual learning methods can reduce computational costs by up to 60\% while maintaining or exceeding the accuracy of standard methods. Our findings suggest that ACs offer a promising avenue for enhancing continual learning models, providing both improved performance and the ability to adapt the network computation in environments where such flexibility might be required.
Related papers
- Temporal-Difference Variational Continual Learning [89.32940051152782]
A crucial capability of Machine Learning models in real-world applications is the ability to continuously learn new tasks.
In Continual Learning settings, models often struggle to balance learning new tasks with retaining previous knowledge.
We propose new learning objectives that integrate the regularization effects of multiple previous posterior estimations.
arXiv Detail & Related papers (2024-10-10T10:58:41Z) - Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal [54.93261535899478]
In real-world applications, such as robotic control of reinforcement learning, the tasks are changing, and new tasks arise in a sequential order.
This situation poses the new challenge of plasticity-stability trade-off for training an agent who can adapt to task changes and retain acquired knowledge.
We propose a rehearsal-based continual diffusion model, called Continual diffuser (CoD), to endow the diffuser with the capabilities of quick adaptation (plasticity) and lasting retention (stability)
arXiv Detail & Related papers (2024-09-04T08:21:47Z) - Normalization and effective learning rates in reinforcement learning [52.59508428613934]
Normalization layers have recently experienced a renaissance in the deep reinforcement learning and continual learning literature.
We show that normalization brings with it a subtle but important side effect: an equivalence between growth in the norm of the network parameters and decay in the effective learning rate.
We propose to make the learning rate schedule explicit with a simple re- parameterization which we call Normalize-and-Project.
arXiv Detail & Related papers (2024-07-01T20:58:01Z) - 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) - 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) - Continual Learning with Pretrained Backbones by Tuning in the Input
Space [44.97953547553997]
The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks.
We propose a novel strategy to make the fine-tuning procedure more effective, by avoiding to update the pre-trained part of the network and learning not only the usual classification head, but also a set of newly-introduced learnable parameters.
arXiv Detail & Related papers (2023-06-05T15:11:59Z) - Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks
in Continual Learning [23.15206507040553]
We propose Auxiliary Network Continual Learning (ANCL) to equip the neural network with the ability to learn the current task.
ANCL applies an additional auxiliary network which promotes plasticity to the continually learned model which mainly focuses on stability.
More concretely, the proposed framework materializes in a regularizer that naturally interpolates between plasticity and stability.
arXiv Detail & Related papers (2023-03-16T17:00:42Z) - New Insights on Relieving Task-Recency Bias for Online Class Incremental
Learning [37.888061221999294]
In all settings, the online class incremental learning (OCIL) is more challenging and can be encountered more frequently in real world.
To strike a preferable trade-off between stability and plasticity, we propose an Adaptive Focus Shifting algorithm.
arXiv Detail & Related papers (2023-02-16T11:52:00Z) - Center Loss Regularization for Continual Learning [0.0]
In general, neural networks lack the ability to learn different tasks sequentially.
Our approach remembers old tasks by projecting the representations of new tasks close to that of old tasks.
We demonstrate that our approach is scalable, effective, and gives competitive performance compared to state-of-the-art continual learning methods.
arXiv Detail & Related papers (2021-10-21T17:46:44Z) - Uniform Priors for Data-Efficient Transfer [65.086680950871]
We show that features that are most transferable have high uniformity in the embedding space.
We evaluate the regularization on its ability to facilitate adaptation to unseen tasks and data.
arXiv Detail & Related papers (2020-06-30T04:39:36Z)
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.