arXiv Machine Learning July 7, 2026 By Abanoub Ghobrial, Kerstin Eder

DIRA-SS:Dynamic Domain Incremental Regularised Adaptation -- Self-Supervised

Read the original on arXiv Machine Learning →

arXiv:2311. 07461v3 Announce Type: replace Abstract: Autonomous systems (AS) often rely on Deep Neural Network (DNN) classifiers to operate in complex and dynamically changing environments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 5

Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment

arXiv:2407. 21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data.

By Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah

llms computer-vision fine-tuning safety

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...

By Jes\'us M Rodr\'iguez-de-Vera, Imanol G Estepa, Ignacio Saras\'ua, Bhalaji Nagarajan, Petia Radeva

llms agents benchmarks

arXiv Machine Learning

Jul 21

DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation

arXiv:2607. 17467v1 Announce Type: cross Abstract: Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples.

By Siobhan Reid, Zhixiang Chi, Li Gu, Omid Reza Heidari, Ziqiang Wang, Yang Wang

fine-tuning benchmarks

arXiv Machine Learning

Sep 22

SPeaR: Test-Time Adaptation with Steering Primitives for Realigning Representations

arXiv:2609.24111v1 Announce Type: new Abstract: Test-time adaptation (TTA) addresses distribution shift using only unlabeled test data. Existing methods typically adapt pretrained models by updating...

By Muhammad Sudipto Siam Dip, Ali Etemad

llms

arXiv Machine Learning

Aug 18

Free-Flow Class-Incremental Learning: Towards Robust CIL under Variable Class Arrivals

arXiv:2604. 02765v2 Announce Type: replace Abstract: Class-incremental learning (CIL) is commonly evaluated under predefined schedules with fixed or nearly equal class increments, leaving irregular class-arrival scenarios underexplored.

By Zhiming Xu, Baile Xu, Jian Zhao, Furao Shen, Suorong Yang

safety

arXiv AI

Aug 12

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning

arXiv:2608. 10804v1 Announce Type: cross Abstract: Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts.

By Qiang Wang, Songlin Dong, Shaokun Wang, Jizhou Han, Xiang Song, Chenhao Ding, Yuhang He, Yihong Gong

benchmarks

One email a morning, machine-written

Related stories

Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment

Precision at Scale: Domain-Specific Datasets On-Demand

DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation

SPeaR: Test-Time Adaptation with Steering Primitives for Realigning Representations

Free-Flow Class-Incremental Learning: Towards Robust CIL under Variable Class Arrivals

BPG: Balancing Plasticity and Generalization for Domain Incremental Learning