arXiv Computer Vision
Signal-Noise Factorization Isolates Nuisance Variation into Removable Subspaces
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DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty
arXiv:2606. 23942v1 Announce Type: new Abstract: We present a large-scale empirical study isolating the contributions of the Derivative Regularization penalty (DREG).
arXiv Machine Learning
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Rethinking the Information Bottleneck: Structured Decomposition under Label-Induced Partitions
The paper proposes a structured version of the Information Bottleneck (IB) that separates label-relevant structure from within-condition variation using a dual-bottleneck formulation. It introduces a conditional KL term that targets within-condition information, allowing explicit control over nuisance-like variation in learned representations. Experiments demonstrate improved performance in low-data classification and consistent gains on dense prediction tasks.
arXiv AI
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Beyond the Hard Budget: Sparsity Regularizers for More Interpretable Top-k Sparse Autoencoders
arXiv:2606. 27321v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have become a leading tool for interpreting the representations of vision foundation models, decomposing their polysemantic activations into a larger set of sparse, more monosemantic features.
arXiv Machine Learning
Aug 27
JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning
JEPAMatch introduces a new semi‑supervised learning framework that replaces traditional output‑thresholding with explicit geometric shaping of latent representations. By combining the FlexMatch loss with a latent‑space regularization inspired by LeJEPA, the method encourages isotropic Gaussian structure in the embedding space, mitigating class imbalance and noisy pseudo‑labels. Experiments on CIFAR‑100, STL‑10, and Tiny‑ImageNet show consistent performance gains and faster convergence compared to existing FixMatch‑based baselines.
arXiv Machine Learning
Jun 9
Energy-Regularized Spatial Masking: A Novel Approach to Enhancing Robustness and Interpretability in Vision Models
arXiv:2604. 06893v3 Announce Type: replace-cross Abstract: Deep convolutional neural networks achieve remarkable performance by exhaustively processing dense spatial feature maps, yet this brute-force strategy introduces significant computational redundancy and encourages reliance on spurious background correlations.
arXiv Computer Vision
Sep 14
DRDN: Decoupled Representation Dynamic Network for From-Scratch ViT Class-Incremental Learning
arXiv:2607.01630v2 Announce Type: replace Abstract: Dynamic expansion methods for class-incremental learning (CIL) protect task-specific knowledge by growing dedicated tokens or subnetworks, yet our...