SnapViT: single-shot network approximation for pruned Vision Transformers is introduced, a new post-pretraining structured pruning method that enables elastic inference across a continuum of compute budgets, and a self-supervised importance scoring mechanism that maintains strong performance without requiring retraining or labels.
LeVJEPA is introduced, the first video encoder trained under LeJEPA's collapse-free objective, which indicates that video becomes a viable and in several respects preferable substrate for general-purpose visual pretraining.
Lukas Kuhn, Lucas Maes, Giuseppe Serra et al.· 3 citations· ⚡1
This work proposes a novel one-shot unlearning approach, abandoning iterative optimization in favor of a direct, exact analytical solution, and achieves state-of-the-art forgetting-utility trade-offs on TOFU-5%, TOFU-10%, MUSE-Books, MUSE-News and WMDP, significantly reducing computational overhead without sacrificing...
Pawel Batorski, P. Spurek, Paul Swoboda· 1 citation
The core finding empirically identifies a data-efficiency frontier: for domain-proximal scenes with>= 200 samples, Swin-Graft surpasses YOLOv11x and CNNs retain advantage for domain-distant scenes.
Efficient Point Masked Autoencoders (EP-MAE), a new framework designed to significantly reduce the training cost of 3D self-supervised pre-training while maintaining strong representation quality, and provides a scalable and effective foundation for future 3D neural network models is presented.
Jian Zhu, Jiale Zhao, Cheng Lin et al.· Neural Networks· 0 citations
A retraining-free VLM pruning framework called PORTA is introduced that derives a task- and modality-agnostic importance formulation based on activation variation, estimated from generic calibration data, which reliably captures feature-level representation utility across modalities.
Minseok Kang, Hyunwoo J. Kim, Chanyoung Kim et al.· 1 citation
Deep neural networks often suffer significant accuracy degradation when exposed to real-world image corruptions and distribution shifts. To overcome the limitations of fixed, input-agnostic test-time augmentation (TTA), an adaptive framework is proposed that learns per-sample transformations via reinforcement learning....
T. Mittal, A. Dubey, Dharmender Saini et al.· Scientific Reports· 0 citations
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