arXiv Machine Learning

Interleaved Noise Injection Improves Clean, Corrupted, and OOD Performance

arXiv:2607. 14466v1 Announce Type: new Abstract: Noise injection is a well-known technique in stochastic optimization.

arXiv Machine Learning

Jul 8

Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning

arXiv:2607. 06151v1 Announce Type: new Abstract: Generalization remains a pivotal challenge in deep learning, where traditional optimizers like Stochastic Gradient Descent (SGD) often converge to sharp minima, leading to overfitting and reduced performance on unseen data.

By Yao Fu, Chunxia Zhang, Junmin Liu, Yihang Jin, Haishan Ye, Yuanao Yang

benchmarks

arXiv AI

Jun 16

ALCL: An Adaptive Log-Correntropy Loss for Robust Learning under Non-Gaussian Noise

arXiv:2606. 16050v1 Announce Type: cross Abstract: Robust deep learning under heavy-tailed and impulsive noise remains challenging because conventional losses such as mean squared error (MSE) exhibit unbounded sensitivity to outliers.

By Mainak Kundu, Ria Kanjilal, Ismail Uysal

benchmarks

arXiv Machine Learning

Jun 11

Simplicity Suffices for Parameter Noise Injection in Stochastic Gradient Descent

arXiv:2606. 12054v1 Announce Type: new Abstract: Injecting noise into the optimization process is a well-established technique for improving the training and generalization of deep neural networks.

By Benjamin Leblanc, Louis-Jacob Lebel, Teddy Kana, Richard Kamel

arXiv Machine Learning

Jun 8

An Adaptive Data cleaning Framework for Noisy Label Detection

arXiv:2606. 07086v1 Announce Type: cross Abstract: Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets.

By Chen-Hsuan Fang, Wei-Hsinag Chen, Pin-Hsuan Yu, Jung-Hua Wang, Tsung-Wei Pan

computer-vision

arXiv Machine Learning

Jul 15

Same Loss, Same Noise, Opposite Schedules: Noise Structure and Optimizer Normalization Jointly Determine Whether Learning-Rate Cooldown Helps

arXiv:2607. 12360v1 Announce Type: new Abstract: The cooldown phase of a warmup-stable-decay (WSD) learning-rate schedule, now a default in large-model pretraining, lowers the final training loss in some settings and does nothing in others.

By Subham Singh, Ashutosh Mishra, Subha Raut

arXiv Machine Learning

Jul 23

Label-Noise Resistant Learning via Optimal Brain Damage Masking

arXiv:2508. 09697v4 Announce Type: replace Abstract: Noisy labels are inevitable in real-world multimedia applications.

By Xinlei Zhang, Fan Liu, Chuanyi Zhang, Xiaoying Ji, Wenhui Wang, Wei Zhou, Yuhui Zheng

efficiency benchmarks

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.

By Hannah Gao (Massachusetts Institute of Technology), Isha Agarwal (Massachusetts Institute of Technology), Dylan Hadfield-Menell (Massachusetts Institute of Technology), Rachel Ma (Massachusetts Institute of Technology)

llms computer-vision fine-tuning multimodal safety

arXiv Machine Learning

Jun 2

RADE: Random Add-Drop Edge as a Regularizer

arXiv:2606. 00757v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) suffer from overfitting and over-squashing of long-range information.

By Danial Saber, Amirali Salehi-Abari

benchmarks safety

arXiv Machine Learning

Jun 2

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs

arXiv:2606. 02267v1 Announce Type: new Abstract: The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment.

By Nicolas Stalder, Benjamin F. Grewe, Matteo Saponati, Pau Vilimelis Aceituno

benchmarks safety

arXiv Machine Learning

Jul 24

Gradient Concentration, Not Weight Saliency, Explains Representation-Level Class Unlearning

arXiv:2607. 21353v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific training data while preserving model utility.

By Billel Habbati, Alessio Merlo, Luca Verderame, Meriem Guerar

efficiency benchmarks

arXiv AI

Jul 24

Benchmarking Unlearning for Vision Transformers

arXiv:2602. 20114v2 Announce Type: replace-cross Abstract: Machine unlearning (MU) refers to the post-training capability to remove (the influence of) training examples that are incorrect, biased, or leak sensitive/private information.

By Kairan Zhao, Iurie Luca, Peter Triantafillou

llms diffusion computer-vision benchmarks

arXiv AI

Jul 21

Reducing Per-Sample Harm in Stochastic Optimization

arXiv:2607. 16261v1 Announce Type: cross Abstract: Modern optimizers combine gradients from the current mini-batch with historical optimization state, such as momentum or adaptive moments.

By Apostolos Avranas

computer-vision benchmarks

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