Latent Spectral Regularization for Continual Learning

• URL: http://arxiv.org/abs/2301.03345v4
• Date: Tue, 16 Jul 2024 12:33:12 GMT
• Title: Latent Spectral Regularization for Continual Learning
• Abstract summary: We study the phenomenon by investigating the geometric characteristics of the learner's latent space. We propose a geometric regularizer that enforces weak requirements on the Laplacian spectrum of the latent space.
• Score: 21.445600749028923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget catastrophically whenever they face a changing training data distribution. Rehearsal-based Continual Learning (CL) approaches have been established as a versatile and reliable solution to overcome this limitation; however, sudden input disruptions and memory constraints are known to alter the consistency of their predictions. We study this phenomenon by investigating the geometric characteristics of the learner's latent space and find that replayed data points of different classes increasingly mix up, interfering with classification. Hence, we propose a geometric regularizer that enforces weak requirements on the Laplacian spectrum of the latent space, promoting a partitioning behavior. Our proposal, called Continual Spectral Regularizer for Incremental Learning (CaSpeR-IL), can be easily combined with any rehearsal-based CL approach and improves the performance of SOTA methods on standard benchmarks.

Related papers

• To Retain or to Adapt? Generalizing Continual Learning [39.95178050304739]
  We argue that in non-stationary environments prioritizing retention can impede real-time adaptation.<n>We formalize CL as an online optimization problem governed by the interaction between environmental and learning dynamics.<n>We propose a new general class of continual learning algorithms, which we call Predictive Continual Learning.
  arXiv  Detail & Related papers  (2026-07-06T20:11:53Z)
• MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models [64.2820121997882]
  MIITA is a Memory-Induced Inference-Time Adaptation framework for supervised CL under constrained storage.<n>It stores supervised experiences as compact correction-direction prototypes with semantic anchors, and retrieves them at inference time using semantic and uncertainty-based cues.<n>Experiments show that MIITA consistently improves final performance and mitigates forgetting under fixed memory budgets.
  arXiv  Detail & Related papers  (2026-05-20T03:03:04Z)
• Online Continual Learning with Dynamic Label Hierarchies [72.33335166136045]
  We introduce a new problem setting, DHOCL, where severity evolve across granularities and each sample provides supervision at a single hierarchical level.<n>In this setting, we find two fundamental issues: (i) partial supervision under mixed granularities provides only point-wise signals over an evolving path-wise hierarchy, which constrains plasticity and undermines cross-level semantic consistency.<n>We propose HALO, which adaptively combines complementary classification heads, regularized by organized learnable hierarchical prototypes, enabling rapid adaptation, hierarchical consistency, and structured knowledge consolidation as the taxonomy evolves.
  arXiv  Detail & Related papers  (2026-05-12T08:20:23Z)
• Forget Less, Retain More: A Lightweight Regularizer for Rehearsal-Based Continual Learning [51.07663354001582]
  Deep neural networks suffer from catastrophic forgetting, where performance on previous tasks degrades after training on a new task.<n>We present a novel approach to address this challenge, focusing on the intersection of memory-based methods and regularization approaches.<n>We formulate a regularization strategy, termed Information Maximization (IM) regularizer, for memory-based continual learning methods.
  arXiv  Detail & Related papers  (2025-12-01T15:56:00Z)
• Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistency [52.52950138164424]
  We show that when leveraging the off-the-shelf (vision) foundation models for feature extraction, the geometric shapes of the resulting feature distributions exhibit remarkable transferability across domains and datasets.<n>We embody our geometric knowledge-guided distribution calibration framework in two popular and challenging settings: federated learning and long-tailed recognition.<n>In long-tailed learning, it utilizes the geometric knowledge transferred from sample-rich categories to recover the true distribution for sample-scarce tail classes.
  arXiv  Detail & Related papers  (2025-08-19T05:22:59Z)
• Overcoming catastrophic forgetting in neural networks [0.0]
  Catastrophic forgetting is the primary challenge that hinders continual learning.<n> Elastic Weight Consolidation is a regularization-based approach inspired by synaptic consolidation in biological neural systems.<n>Our results confirm what was shown in previous research, showing that EWC significantly reduces forgetting compared to naive training.
  arXiv  Detail & Related papers  (2025-07-14T17:04:05Z)
• Weight Factorization and Centralization for Continual Learning in Speech Recognition [55.63455095283984]
  Continually training the models in a rehearsal-free, multilingual, and language agnostic condition, likely leads to catastrophic forgetting.<n>Inspired by the ability of human brains to learn and consolidate knowledge through the waking-sleeping cycle, we propose a continual learning approach.
  arXiv  Detail & Related papers  (2025-06-19T19:59:24Z)
• CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection [21.952352543304592]
  Anomaly detection (AD) has attracted remarkable attention in hyperspectral image (HSI) processing fields.<n>Most existing deep learning (DL)-based algorithms indicate dramatic potential for detecting anomaly samples through specific training process under current scenario.<n>However, the limited prior information and the catastrophic forgetting problem indicate crucial challenges for existing DL structure in open scenarios cross-domain detection.
  arXiv  Detail & Related papers  (2025-05-17T02:32:41Z)
• Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective [52.662463893268225]
  Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. Existing SHGL methods encounter two significant limitations. We introduce a novel framework enhanced by rank and dual consistency constraints.
  arXiv  Detail & Related papers  (2024-12-01T09:33:20Z)
• Continual Task Learning through Adaptive Policy Self-Composition [54.95680427960524]
  CompoFormer is a structure-based continual transformer model that adaptively composes previous policies via a meta-policy network. Our experiments reveal that CompoFormer outperforms conventional continual learning (CL) methods, particularly in longer task sequences.
  arXiv  Detail & Related papers  (2024-11-18T08:20:21Z)
• 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)
• SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training [68.7896349660824]
  We present an in-depth analysis of the progressive overfitting problem from the lens of Seq FT. Considering that the overly fast representation learning and the biased classification layer constitute this particular problem, we introduce the advanced Slow Learner with Alignment (S++) framework. Our approach involves a Slow Learner to selectively reduce the learning rate of backbone parameters, and a Alignment to align the disjoint classification layers in a post-hoc fashion.
  arXiv  Detail & Related papers  (2024-08-15T17:50:07Z)
• Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection [64.4350027428928]
  We propose a novel uncertainty-guided class imbalance learning framework for imbalanced social event detection tasks. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.
  arXiv  Detail & Related papers  (2023-10-30T03:32:04Z)
• Conditional Kernel Imitation Learning for Continuous State Environments [9.750698192309978]
  We introduce a novel conditional kernel density estimation-based imitation learning framework. We show consistently superior empirical performance over many state-of-the-art IL algorithms.
  arXiv  Detail & Related papers  (2023-08-24T05:26:42Z)
• Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants [166.916517335816]
  In this paper, we offer a unified solution to the misalignment dilemma in the three tasks. We propose neural collapse terminus that is a fixed structure with the maximal equiangular inter-class separation for the whole label space. Our method holds the neural collapse optimality in an incremental fashion regardless of data imbalance or data scarcity.
  arXiv  Detail & Related papers  (2023-08-03T13:09:59Z)
• Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering [15.678104431835772]
  We take an initial step toward a theoretical understanding of relational knowledge distillation (RKD) For semi-supervised learning, we demonstrate the label efficiency of RKD through a general framework of cluster-aware learning. We show that despite the common effect of learning accurate clusterings, RKD facilitates a "global" perspective.
  arXiv  Detail & Related papers  (2023-07-20T17:05:51Z)
• Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection [37.99031842449251]
  Video anomaly detection under weak supervision presents significant challenges. We present a weakly supervised anomaly detection framework that focuses on efficient context modeling and enhanced semantic discriminability. Our approach significantly improves the detection accuracy of certain anomaly sub-classes, underscoring its practical value and efficacy.
  arXiv  Detail & Related papers  (2023-06-26T06:45:16Z)
• Mitigating Forgetting in Online Continual Learning via Contrasting Semantically Distinct Augmentations [22.289830907729705]
  Online continual learning (OCL) aims to enable model learning from a non-stationary data stream to continuously acquire new knowledge as well as retain the learnt one. Main challenge comes from the "catastrophic forgetting" issue -- the inability to well remember the learnt knowledge while learning the new ones.
  arXiv  Detail & Related papers  (2022-11-10T05:29:43Z)
• Reparameterized Variational Divergence Minimization for Stable Imitation [57.06909373038396]
  We study the extent to which variations in the choice of probabilistic divergence may yield more performant ILO algorithms. We contribute a re parameterization trick for adversarial imitation learning to alleviate the challenges of the promising $f$-divergence minimization framework. Empirically, we demonstrate that our design choices allow for ILO algorithms that outperform baseline approaches and more closely match expert performance in low-dimensional continuous-control tasks.
  arXiv  Detail & Related papers  (2020-06-18T19:04:09Z)

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.