← Back to all news
arXiv Machine Learning

Post-Training Augmentation Invariance

arXiv:2505. 11702v3 Announce Type: replace Abstract: This work develops a framework for post-training augmentation invariance, in which our goal is to add invariance properties to a pretrained network without altering its behavior on the original, non-augmented input distribution.

Related stories

arXiv Machine Learning
1d ago

CrossGMN: Graph Metanetworks for Cross-Architecture Weight-Space Transformations

CrossGMN introduces a graph metanetwork that processes a trained source network and an initialized target network simultaneously, enabling equivariant cross‑architecture weight‑space transformations. By preserving symmetry through cross‑network message passing, CrossGMN can refine target network initializations while remaining invariant to source permutations and equivariant to target permutations. Experiments demonstrate that CrossGMN accelerates knowledge distillation, transfers across datasets without retraining, and unifies compression from diverse source architectures into a common target architecture.

By Adir Dayan, Yam Eitan, Haggai Maron
llmscomputer-visionefficiency
More like this →
arXiv Machine Learning
Sep 21

On the Limits of Maximal Coding Rate Reduction for Out-of-Distribution Generalisation

The paper investigates the limits of the maximal coding rate reduction (MCR²) framework for out‑of‑distribution (OOD) generalisation. It shows that MCR² can lead to complete prediction failure under distribution shift, even when a perfectly stable feature is available, and that adding invariance principles from IRM or REx does not resolve this issue. The authors conclude that additional assumptions or learning principles are needed to guarantee stable OOD predictions with MCR².

By Menghui Zhou, Gaoshan Bi, Vitaveska Lanfranchi, Po Yang
safety
More like this →
arXiv AI
3d ago

Unmerge: Efficient Machine Unlearning via Task Arithmetic

The paper introduces Unmerge, an efficient machine unlearning algorithm that treats unlearning as the inverse of task arithmetic. By representing the forget component as a low‑rank basis at each layer, Unmerge optimizes three goals—matching the merged vector, suppressing leakage, and bounding correction size—to limit forget leakage and retain damage. Experiments on ResNet‑50, ViT‑S/16, and Llama‑3.2‑3B show significant performance gains over existing methods while maintaining privacy and feature‑distribution fidelity.

By Haoran Tang, Andrew Tan, Rajiv Khanna
llmsfine-tuning
More like this →
arXiv Machine Learning
Sep 7

An Analysis of Self-supervised Pre-training with Dependent Samples

The paper investigates self‑supervised pre‑training that uses multiple data augmentations of the same unlabeled sample. It shows that pooling these dependent augmentations together yields statistical estimation error bounds that are never worse than, and sometimes better than, partitioning the data into independent subsets. The analysis explains why using many augmentations is practically advantageous, especially when their correlations have mild effects or reduce estimation variance.

By Maximilian Fleissner, Debarghya Ghoshdastidar, Samory Kpotufe
More like this →