arXiv Machine Learning
Jul 2
AdaBoosting Text Prompts for Vision-Language Models
arXiv:2607. 00684v1 Announce Type: new Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts.
Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels
arXiv:2604. 06614v2 Announce Type: replace-cross Abstract: Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream tasks.
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arXiv AI
Jul 28
Flick: Few Labels Text Classification using K-Aware Intermediate Learning in Multi-Task Low-Resource Languages
arXiv:2506. 10292v2 Announce Type: replace-cross Abstract: Training deep learning networks with minimal supervision has gained significant research attention due to its potential to reduce reliance on extensive labelled data.
arXiv AI
Jun 4
EvoPrompt: Guided Prompt Evolution for Vision-Language Models Adaptation
arXiv:2603. 09493v2 Announce Type: replace-cross Abstract: The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge.
arXiv Machine Learning
Jul 2
MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation
arXiv:2602. 21397v2 Announce Type: replace-cross Abstract: Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrained weights.
arXiv Machine Learning
Jun 30
Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting
arXiv:2505. 16903v2 Announce Type: replace Abstract: Prompt tuning has become a key mechanism for adapting pre-trained Graph Neural Networks (GNNs) to new downstream tasks.
arXiv AI
Jul 1
The Label Imitation Game: Turing Test Network for Zero-Shot Pseudo-Label Pruning
arXiv:2606. 30875v1 Announce Type: cross Abstract: Foundation model pseudo-labeling - labeling data strictly via zero-shot inference - enables massive scale, but performance is undermined by hallucinations that evade standard thresholds.
arXiv Machine Learning
Aug 10
Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions
arXiv:2608. 06896v1 Announce Type: new Abstract: Deep learning has achieved great success in recent years thanks to the availability of high-quality, well-annotated training data.
Hugging Face Trending Papers
Jul 20
PRiSM: Prototype Regularization for Few-Shot VLMs
Training-free few-shot adaptation methods have gained significant attention recently in the context of Vision-language Models (VLMs). Yet, current benchmarks rely on strong assumptions about the statistics of the adaptation data, e.
arXiv AI
Jun 2
ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning
arXiv:2603. 09692v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become the standard for aligning Large Language Models (LLMs), yet its efficacy is bottlenecked by the high cost of acquiring preference data, especially in low-resource and expert domains.
arXiv AI
Jun 15
Learning What to Predict: Downstream-Guided Task Design for Continued Pretraining
arXiv:2601. 22108v2 Announce Type: replace-cross Abstract: Continued pretraining is optimized with fixed self-supervised tasks but selected by downstream performance, creating a coarse feedback loop in which practitioners evaluate checkpoints, change data mixtures or objectives, and restart runs, while individual updates remain blind to target capabilities.
arXiv AI
22h ago
Learning What Not to Learn: Adversarial Disentangled Prompt Tuning for Robust Vision-Language Models
arXiv:2608. 17306v1 Announce Type: cross Abstract: While adversarial prompt tuning can enhance robustness of vision-language models efficiently, we find that existing methods aggravate robust generalization overfitting on seen classes, leading to a rapid degradation in performance against adversarial examples of unseen classes as training progresses.
arXiv AI
Jun 11
CoVar: Confidence-Variance-Guided Pseudo-Label Selection for Semi-Supervised Learning
arXiv:2601. 11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance.