arXiv AI
Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 18
From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging
arXiv:2506. 14126v2 Announce Type: replace-cross Abstract: Modern deep learning is increasingly characterized by the use of open-weight foundation models that can be fine-tuned on specialized datasets.
arXiv Machine Learning
Aug 4
Generated Images Are Easier to Forget: A Machine Unlearning Perspective for Synthetic Image Detection
arXiv:2608. 00716v1 Announce Type: cross Abstract: Robust detection of generated images is critical to counter the misuse of generative models.
arXiv AI
Sep 10
Precision at Scale: Domain-Specific Datasets On-Demand
arXiv:2407.03463v2 Announce Type: replace-cross Abstract: In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets fo...
arXiv AI
Jun 9
A Mechanistic Analysis of Adversarial Fine-tuning of Vision Transformers
arXiv:2606. 07593v1 Announce Type: cross Abstract: The widespread use of image classification models in high-risk, real-world situations necessitates making these models robust to slight disturbances or perturbations, such as blurring or sharpening, in the input images.
arXiv Machine Learning
Jul 1
A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning
arXiv:2602. 20062v2 Announce Type: replace Abstract: Pretraining and fine-tuning are central stages in modern machine learning systems.
arXiv Machine Learning
Aug 24
Generalization Measures under Controlled Covariate Shift: A Regime-Aware Benchmark
arXiv:2602.01718v2 Announce Type: replace Abstract: Predicting generalization from quantities available before target-test evaluation remains a central challenge in deep learning. The systematic benc...