The paper investigates the function of the projector in contrastive self‑supervised learning by analyzing rank dynamics in the encoder and projector. It finds that rank reduction mainly occurs in the last layer and proposes a targeted weight regularization for that layer, which outperforms global orthogonal regularization. Experiments on SimCLR with ImageNet100 and CIFAR datasets show more than 1% Top‑1 accuracy improvement and consistent gains over baseline variants.
By Siladittya Manna, Priyangshu Mandal, Umapada Pal, Saumik Bhattacharya
The paper introduces SACReg, a spectral anti-collapse regularizer that enforces λ-balance across weight matrices to prevent dimensional collapse in the backbone of joint-embedding self-supervised learning models. Applied to JEPA, the resulting λ-JEPA improves ImageNet-1k classification and linear-probe transfer on eight image datasets, and also outperforms prior video self-supervised methods on Something-Something-v2 and Kinetics-400.
By Berker Demirel, Cl\'ementine Domin\'e, Valentino Maiorca, Marco Fumero, Marco Mondelli, Francesco Locatello
Dynamic expansion methods for class-incremental learning (CIL) protect task-specific knowledge by growing dedicated tokens or subnetworks, yet our analyses suggest that classification supervision alone does not sufficiently preserve task-agnostic shared backbone representations over long incremental sequences. We identify two intertwined challenges: cross-task confusion from sequential training on predominantly current-task data, which biases decision boundaries toward recent tasks; and under-optimized shared representations in the backbone that cap long-term discriminability as tasks accumulate.
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...
By Bingchen Huang, Yifu Chen, Zhiling Wang, Yuanchao Du
arXiv:2609.06460v1 Announce Type: new
Abstract: Recently, Masked Pretraining (MPT) based on reconstruction pretraining tasks has risen to a promising self-supervised learning paradigm across various...
By Qi Zhang, Runyu Zhou, Yifei Wang, Yisen Wang
arXiv:2608. 08309v1 Announce Type: cross Abstract: We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: semantic invariance across augmented views, patch-level spatial prediction, and representational non-degeneracy.
By Nikos Giakoumoglou, Paschalis Giakoumoglou, Tania Stathaki
We investigate how each component of the Transformer feedforward block architecture design determines how much rank survives across depth at initialization. We reinterpret skip connections and normalization, long understood as controlling magnitude, as mechanisms for preserving gradient rank across depth, since the very matrix multiplications and nonlinear activations that make the network expressive also reduce the rank.
arXiv:2607. 14018v1 Announce Type: cross Abstract: We investigate how each component of the Transformer feedforward block architecture design determines how much rank survives across depth at initialization.
By Katie Everett
arXiv:2609.06341v1 Announce Type: cross
Abstract: Linear algebra provides the framework of concepts (matrix rank, singular value decomposition (SVD), and eigendecomposition) that modern artificial in...
By Anjaneya Teja Sarma Kalvakolanu
arXiv:2606. 29951v1 Announce Type: new Abstract: Interpretable Mesomorphic Neural Networks (IMNs) offer a promising framework that combines the predictive power of deep neural networks with the interpretability of linear models.
By Hugo L. Hammer, Vajira Thambawita, Kristoffer Herland Hellton, P{\aa}l Halvorsen
arXiv:2606. 11123v1 Announce Type: new Abstract: Backpropagation (BP) is widely viewed as biologically implausible, in part because it requires feedback weights to be the transpose of forward weights for error propagation.
By Gauthier Boeshertz, Razvan Pascanu, Claudia Clopath
arXiv:2607. 16295v1 Announce Type: cross Abstract: Mechanistic interpretability has made significant strides in understanding neural network representations, with sparse dictionary learning (SDL) methods, most prominently sparse autoencoders, as a central paradigm.
By Yiming Tang, Qinglin Qi, Zhaoqian Yao, Harshvardhan Saini, Dianbo Liu