Self-Supervised Training Enhances Online Continual Learning

• URL: http://arxiv.org/abs/2103.14010v1
• Date: Thu, 25 Mar 2021 17:45:27 GMT
• Title: Self-Supervised Training Enhances Online Continual Learning
• Authors: Jhair Gallardo, Tyler L. Hayes, Christopher Kanan
• Abstract summary: In continual learning, a system must incrementally learn from a non-stationary data stream without catastrophic forgetting. Self-supervised pre-training could yield features that generalize better than supervised learning. Our best system achieves a 14.95% relative increase in top-1 accuracy on class incremental ImageNet over the prior state of the art for online continual learning.
• Score: 37.91734641808391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In continual learning, a system must incrementally learn from a non-stationary data stream without catastrophic forgetting. Recently, multiple methods have been devised for incrementally learning classes on large-scale image classification tasks, such as ImageNet. State-of-the-art continual learning methods use an initial supervised pre-training phase, in which the first 10% - 50% of the classes in a dataset are used to learn representations in an offline manner before continual learning of new classes begins. We hypothesize that self-supervised pre-training could yield features that generalize better than supervised learning, especially when the number of samples used for pre-training is small. We test this hypothesis using the self-supervised MoCo-V2 and SwAV algorithms. On ImageNet, we find that both outperform supervised pre-training considerably for online continual learning, and the gains are larger when fewer samples are available. Our findings are consistent across three continual learning algorithms. Our best system achieves a 14.95% relative increase in top-1 accuracy on class incremental ImageNet over the prior state of the art for online continual learning.

Related papers

• Random Representations Outperform Online Continually Learned Representations [68.42776779425978]
  We show that existing online continually trained deep networks produce inferior representations compared to a simple pre-defined random transforms. Our method, called RanDumb, significantly outperforms state-of-the-art continually learned representations across all online continual learning benchmarks. Our study reveals the significant limitations of representation learning, particularly in low-exemplar and online continual learning scenarios.
  arXiv  Detail & Related papers  (2024-02-13T22:07:29Z)
• PILOT: A Pre-Trained Model-Based Continual Learning Toolbox [71.63186089279218]
  This paper introduces a pre-trained model-based continual learning toolbox known as PILOT. On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt. On the other hand, PILOT fits typical class-incremental learning algorithms within the context of pre-trained models to evaluate their effectiveness.
  arXiv  Detail & Related papers  (2023-09-13T17:55:11Z)
• EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones [80.662250618795]
  This paper presents a new curriculum learning approach for the efficient training of visual backbones (e.g., vision Transformers) As an off-the-shelf method, it reduces the wall-time training cost of a wide variety of popular models by >1.5x on ImageNet-1K/22K without sacrificing accuracy.
  arXiv  Detail & Related papers  (2022-11-17T17:38:55Z)
• A Simple Baseline that Questions the Use of Pretrained-Models in Continual Learning [30.023047201419825]
  Some methods design continual learning mechanisms on the pre-trained representations and only allow minimum updates or even no updates of the backbone models during the training of continual learning. We argue that the pretrained feature extractor itself can be strong enough to achieve a competitive or even better continual learning performance on Split-CIFAR100 and CoRe 50 benchmarks. This baseline achieved 88.53% on 10-Split-CIFAR-100, surpassing most state-of-the-art continual learning methods that are all using the same pretrained transformer model.
  arXiv  Detail & Related papers  (2022-10-10T04:19:53Z)
• SLIP: Self-supervision meets Language-Image Pre-training [79.53764315471543]
  We study whether self-supervised learning can aid in the use of language supervision for visual representation learning. We introduce SLIP, a multi-task learning framework for combining self-supervised learning and CLIP pre-training. We find that SLIP enjoys the best of both worlds: better performance than self-supervision and language supervision.
  arXiv  Detail & Related papers  (2021-12-23T18:07:13Z)
• Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data [101.6195176510611]
  "Online" continual learning enables evaluating both information retention and online learning efficacy. In online continual learning, each incoming small batch of data is first used for testing and then added to the training set, making the problem truly online. We introduce a new benchmark for online continual visual learning that exhibits large scale and natural distribution shifts.
  arXiv  Detail & Related papers  (2021-08-20T06:17:20Z)
• Continual Contrastive Self-supervised Learning for Image Classification [10.070132585425938]
  Self-supervise learning method shows tremendous potential on visual representation without any labeled data at scale. To improve the visual representation of self-supervised learning, larger and more varied data is needed. In this paper, we make the first attempt to implement the continual contrastive self-supervised learning by proposing a rehearsal method.
  arXiv  Detail & Related papers  (2021-07-05T03:53:42Z)
• A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks [1.1802674324027231]
  Self-supervised pre-training for transfer learning is becoming an increasingly popular technique to improve state-of-the-art results using unlabeled data. We provide an overview of the taxonomy for self-supervised learning and transfer learning, and highlight some prominent methods for designing pre-training tasks across different domains.
  arXiv  Detail & Related papers  (2020-07-01T22:55:48Z)

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.