New Insights for the Stability-Plasticity Dilemma in Online Continual Learning

• URL: http://arxiv.org/abs/2302.08741v1
• Date: Fri, 17 Feb 2023 07:43:59 GMT
• Title: New Insights for the Stability-Plasticity Dilemma in Online Continual Learning
• Authors: Dahuin Jung, Dongjin Lee, Sunwon Hong, Hyemi Jang, Ho Bae, Sungroh Yoon
• Abstract summary: We propose an online continual learning framework named multi-scale feature adaptation network (MuFAN) MuFAN outperforms other state-of-the-art continual learning methods on the SVHN, CIFAR100, miniImageNet, and CORe50 datasets.
• Score: 21.664470275289407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenario of online continual learning, wherein data comes strictly in a streaming manner, the plasticity of online continual learning is more vulnerable than offline continual learning because the training signal that can be obtained from a single data point is limited. To overcome the stability-plasticity dilemma in online continual learning, we propose an online continual learning framework named multi-scale feature adaptation network (MuFAN) that utilizes a richer context encoding extracted from different levels of a pre-trained network. Additionally, we introduce a novel structure-wise distillation loss and replace the commonly used batch normalization layer with a newly proposed stability-plasticity normalization module to train MuFAN that simultaneously maintains high plasticity and stability. MuFAN outperforms other state-of-the-art continual learning methods on the SVHN, CIFAR100, miniImageNet, and CORe50 datasets. Extensive experiments and ablation studies validate the significance and scalability of each proposed component: 1) multi-scale feature maps from a pre-trained encoder, 2) the structure-wise distillation loss, and 3) the stability-plasticity normalization module in MuFAN. Code is publicly available at https://github.com/whitesnowdrop/MuFAN.

Related papers

• LLMs Can Evolve Continually on Modality for X-Modal Reasoning [62.2874638875554]
  Existing methods rely heavily on modal-specific pretraining and joint-modal tuning, leading to significant computational burdens when expanding to new modalities. We propose PathWeave, a flexible and scalable framework with modal-Path sWitching and ExpAnsion abilities. PathWeave performs comparably to state-of-the-art MLLMs while concurrently reducing parameter training burdens by 98.73%.
  arXiv  Detail & Related papers  (2024-10-26T13:19:57Z)
• 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)
• Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning [33.560003528712414]
  Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data. This poses a challenge in striking a balance between stability and plasticity when adapting to new information. We propose Branch-tuning, an efficient and straightforward method that achieves a balance between stability and plasticity in continual SSL.
  arXiv  Detail & Related papers  (2024-03-27T05:38:48Z)
• RanDumb: A Simple Approach that Questions the Efficacy of Continual Representation Learning [68.42776779425978]
  We show that existing online continually trained deep networks produce inferior representations compared to a simple pre-defined random transforms. We then train a simple linear classifier on top without storing any exemplars, processing one sample at a time in an online continual learning setting. 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)
• New metrics for analyzing continual learners [27.868967961503962]
  Continual Learning (CL) poses challenges to standard learning algorithms. This stability-plasticity dilemma remains central to CL and multiple metrics have been proposed to adequately measure stability and plasticity separately. We propose new metrics that account for the task's increasing difficulty.
  arXiv  Detail & Related papers  (2023-09-01T13:53:33Z)
• CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation [128.00940554196976]
  Vision-Language Continual Pretraining (VLCP) has shown impressive results on diverse downstream tasks by offline training on large-scale datasets. To support the study of Vision-Language Continual Pretraining (VLCP), we first contribute a comprehensive and unified benchmark dataset P9D. The data from each industry as an independent task supports continual learning and conforms to the real-world long-tail nature to simulate pretraining on web data.
  arXiv  Detail & Related papers  (2023-08-14T13:53:18Z)
• IF2Net: Innately Forgetting-Free Networks for Continual Learning [49.57495829364827]
  Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, we investigated how to design an Innately Forgetting-Free Network (IF2Net) IF2Net allows a single network to inherently learn unlimited mapping rules without telling task identities at test time.
  arXiv  Detail & Related papers  (2023-06-18T05:26:49Z)
• 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)
• 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)
• Enabling Continual Learning with Differentiable Hebbian Plasticity [18.12749708143404]
  Continual learning is the problem of sequentially learning new tasks or knowledge while protecting previously acquired knowledge. catastrophic forgetting poses a grand challenge for neural networks performing such learning process. We propose a Differentiable Hebbian Consolidation model which is composed of a Differentiable Hebbian Plasticity.
  arXiv  Detail & Related papers  (2020-06-30T06:42:19Z)

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.