Look-Ahead Selective Plasticity for Continual Learning of Visual Tasks
- URL: http://arxiv.org/abs/2311.01617v1
- Date: Thu, 2 Nov 2023 22:00:23 GMT
- Title: Look-Ahead Selective Plasticity for Continual Learning of Visual Tasks
- Authors: Rouzbeh Meshkinnejad, Jie Mei, Daniel Lizotte, Yalda Mohsenzadeh
- Abstract summary: We propose a new mechanism that takes place during task boundaries, i.e., when one task finishes and another starts.
We evaluate the proposed methods on benchmark computer vision datasets including CIFAR10 and TinyImagenet.
- Score: 9.82510084910641
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Contrastive representation learning has emerged as a promising technique for
continual learning as it can learn representations that are robust to
catastrophic forgetting and generalize well to unseen future tasks. Previous
work in continual learning has addressed forgetting by using previous task data
and trained models. Inspired by event models created and updated in the brain,
we propose a new mechanism that takes place during task boundaries, i.e., when
one task finishes and another starts. By observing the redundancy-inducing
ability of contrastive loss on the output of a neural network, our method
leverages the first few samples of the new task to identify and retain
parameters contributing most to the transfer ability of the neural network,
freeing up the remaining parts of the network to learn new features. We
evaluate the proposed methods on benchmark computer vision datasets including
CIFAR10 and TinyImagenet and demonstrate state-of-the-art performance in the
task-incremental, class-incremental, and domain-incremental continual learning
scenarios.
Related papers
- Towards Scalable and Versatile Weight Space Learning [51.78426981947659]
This paper introduces the SANE approach to weight-space learning.
Our method extends the idea of hyper-representations towards sequential processing of subsets of neural network weights.
arXiv Detail & Related papers (2024-06-14T13:12:07Z) - Negotiated Representations to Prevent Forgetting in Machine Learning
Applications [0.0]
Catastrophic forgetting is a significant challenge in the field of machine learning.
We propose a novel method for preventing catastrophic forgetting in machine learning applications.
arXiv Detail & Related papers (2023-11-30T22:43:50Z) - 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) - Learning Bayesian Sparse Networks with Full Experience Replay for
Continual Learning [54.7584721943286]
Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered.
Existing CL approaches often keep a buffer of previously-seen samples, perform knowledge distillation, or use regularization techniques towards this goal.
We propose to only activate and select sparse neurons for learning current and past tasks at any stage.
arXiv Detail & Related papers (2022-02-21T13:25:03Z) - 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) - Gradient Projection Memory for Continual Learning [5.43185002439223]
The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems.
We propose a novel approach where a neural network learns new tasks by taking gradient steps in the orthogonal direction to the gradient subspaces deemed important for the past tasks.
arXiv Detail & Related papers (2021-03-17T16:31:29Z) - 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) - Parrot: Data-Driven Behavioral Priors for Reinforcement Learning [79.32403825036792]
We propose a method for pre-training behavioral priors that can capture complex input-output relationships observed in successful trials.
We show how this learned prior can be used for rapidly learning new tasks without impeding the RL agent's ability to try out novel behaviors.
arXiv Detail & Related papers (2020-11-19T18:47:40Z) - Unsupervised Transfer Learning for Spatiotemporal Predictive Networks [90.67309545798224]
We study how to transfer knowledge from a zoo of unsupervisedly learned models towards another network.
Our motivation is that models are expected to understand complex dynamics from different sources.
Our approach yields significant improvements on three benchmarks fortemporal prediction, and benefits the target even from less relevant ones.
arXiv Detail & Related papers (2020-09-24T15:40:55Z) - Adversarially-Trained Deep Nets Transfer Better: Illustration on Image
Classification [53.735029033681435]
Transfer learning is a powerful methodology for adapting pre-trained deep neural networks on image recognition tasks to new domains.
In this work, we demonstrate that adversarially-trained models transfer better than non-adversarially-trained models.
arXiv Detail & Related papers (2020-07-11T22:48:42Z)
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.