arXiv AI August 18, 2026 By Zsombor B\'anfi, Andr\'as G\'ezsi, Andr\'as Formanek

GATTA: Graph Active Learning with Test-Time Augmentation

arXiv:2608. 15084v1 Announce Type: cross Abstract: Test-time augmentation (TTA) has proven effective for improving model robustness and uncertainty estimation in computer vision, yet its application to graph-structured data remains largely unexplored.

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 AI.

computer-vision

arXiv AI

Sep 1

ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning

arXiv:2608.28923v1 Announce Type: cross Abstract: Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, eithe...

By Noah Videcrantz, Mostafa Mehdipour Ghazi

llms fine-tuning benchmarks

arXiv Machine Learning

Sep 10

Rethinking One-Shot Federated Graph Learning: Training-Free Statistical Estimation

arXiv:2609.06154v1 Announce Type: new Abstract: One-shot federated graph learning generally aims to train Graph Neural Networks (GNNs) across clients with disconnected subgraphs in a single communica...

By Shutong Zheng, Sijia Chen

efficiency benchmarks

arXiv Machine Learning

Sep 15

Data-Efficient Agentic Graph Domain Adaptation via Reliability-Aware Prototype Learning

arXiv:2609.14045v1 Announce Type: new Abstract: Agentic learning systems are often required to adapt after deployment by observing new data and reusing prior knowledge under limited supervision or fe...

By Yingxu Wang, Kunyu Zhang, Siyang Gao

rag agents fine-tuning benchmarks

arXiv AI

Jun 2

Learning-To-Measure: In-Context Active Feature Acquisition

arXiv:2510. 12624v2 Announce Type: replace-cross Abstract: Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire.

By Yuta Kobayashi, Zilin Jing, Jiayu Yao, Hongseok Namkoong, Shalmali Joshi

agents benchmarks

arXiv AI

Jun 8

SS-TPT: Stability and Suitability-Guided Test-Time Prompt Tuning for Adversarially Robust Vision-Language Models

arXiv:2606. 06943v1 Announce Type: cross Abstract: Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but remain highly fragile under adversarial perturbations.

By Sunoh Kim, Daeho Um

llms efficiency multimodal benchmarks safety

arXiv Computer Vision

Sep 10

One Loop, Two Gains: Can Active Learning win the Lottery for Free?

arXiv:2609.10311v1 Announce Type: cross Abstract: The lottery ticket hypothesis posits the existence of winning tickets: sparse subnetworks that, when trained in isolation from their original initial...

By Benedikt Tscheschner, Eduardo Veas, Marc Masana

computer-vision fine-tuning efficiency

One email a morning, machine-written

Related stories

ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning

Rethinking One-Shot Federated Graph Learning: Training-Free Statistical Estimation

Data-Efficient Agentic Graph Domain Adaptation via Reliability-Aware Prototype Learning

Learning-To-Measure: In-Context Active Feature Acquisition

SS-TPT: Stability and Suitability-Guided Test-Time Prompt Tuning for Adversarially Robust Vision-Language Models

One Loop, Two Gains: Can Active Learning win the Lottery for Free?