New Insights on Reducing Abrupt Representation Change in Online Continual Learning

• URL: http://arxiv.org/abs/2203.03798v1
• Date: Tue, 8 Mar 2022 01:37:00 GMT
• Title: New Insights on Reducing Abrupt Representation Change in Online Continual Learning
• Authors: Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, Eugene Belilovsky
• Abstract summary: We focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream. We show that applying Experience Replay causes the newly added classes' representations to overlap significantly with the previous classes. We propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes.
• Score: 69.05515249097208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Experience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. We shed new light on this question by showing that applying ER causes the newly added classes' representations to overlap significantly with the previous classes, leading to highly disruptive parameter updates. Based on this empirical analysis, we propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes. We show that using an asymmetric update rule pushes new classes to adapt to the older ones (rather than the reverse), which is more effective especially at task boundaries, where much of the forgetting typically occurs. Empirical results show significant gains over strong baselines on standard continual learning benchmarks

Related papers

• Strike a Balance in Continual Panoptic Segmentation [60.26892488010291]
  We introduce past-class backtrace distillation to balance the stability of existing knowledge with the adaptability to new information. We also introduce a class-proportional memory strategy, which aligns the class distribution in the replay sample set with that of the historical training data. We present a new method named Continual Panoptic Balanced (BalConpas)
  arXiv  Detail & Related papers  (2024-07-23T09:58:20Z)
• 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)
• Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning [100.7407460674153]
  Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks. To mitigate the problem, a line of methods propose to replay the data of experienced tasks when learning new tasks. However, it is not expected in practice considering the memory constraint or data privacy issue. As a replacement, data-free data replay methods are proposed by inverting samples from the classification model.
  arXiv  Detail & Related papers  (2024-01-12T12:51:12Z)
• Mitigating Catastrophic Forgetting in Task-Incremental Continual Learning with Adaptive Classification Criterion [50.03041373044267]
  We propose a Supervised Contrastive learning framework with adaptive classification criterion for Continual Learning. Experiments show that CFL achieves state-of-the-art performance and has a stronger ability to overcome compared with the classification baselines.
  arXiv  Detail & Related papers  (2023-05-20T19:22:40Z)
• Multi-Granularity Regularized Re-Balancing for Class Incremental Learning [32.52884416761171]
  Deep learning models suffer from catastrophic forgetting when learning new tasks. Data imbalance between old and new classes is a key issue that leads to performance degradation of the model. We propose an assumption-agnostic method, Multi-Granularity Regularized re-Balancing, to address this problem.
  arXiv  Detail & Related papers  (2022-06-30T11:04:51Z)
• Probing Representation Forgetting in Supervised and Unsupervised Continual Learning [14.462797749666992]
  Catastrophic forgetting is associated with an abrupt loss of knowledge previously learned by a model. We show that representation forgetting can lead to new insights on the effect of model capacity and loss function used in continual learning.
  arXiv  Detail & Related papers  (2022-03-24T23:06:08Z)
• Reducing Representation Drift in Online Continual Learning [87.71558506591937]
  We study the online continual learning paradigm, where agents must learn from a changing distribution with constrained memory and compute. In this work we instead focus on the change in representations of previously observed data due to the introduction of previously unobserved class samples in the incoming data stream.
  arXiv  Detail & Related papers  (2021-04-11T15:19:30Z)
• ZS-IL: Looking Back on Learned ExperiencesFor Zero-Shot Incremental Learning [9.530976792843495]
  We propose an on-call transfer set to provide past experiences whenever a new class arises in the data stream. ZS-IL demonstrates significantly better performance on the well-known datasets (CIFAR-10, Tiny-ImageNet) in both Task-IL and Class-IL settings.
  arXiv  Detail & Related papers  (2021-03-22T22:43:20Z)

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.