#training-free-methods News & Analysis

3 articles tagged with #training-free-methods. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · Apr 157/10

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Decoding by Perturbation: Mitigating MLLM Hallucinations via Dynamic Textual Perturbation

Researchers introduce Decoding by Perturbation (DeP), a training-free method that reduces hallucinations in multimodal large language models by applying controlled textual perturbations during decoding. The approach addresses the core issue where language priors override visual evidence, achieving improvements across multiple benchmarks without requiring model retraining or visual manipulation.

AINeutralarXiv – CS AI · 3d ago6/10

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AdaMerge: Salience-Aware Adaptive Token Merging for Training-Free Acceleration of Vision Transformers

AdaMerge introduces a training-free method to accelerate Vision Transformers by improving token merging through salience-aware mechanisms and adaptive layer-wise compression. The approach outperforms existing token reduction methods across all computational efficiency benchmarks, maintaining superior accuracy-to-FLOPs ratios on ImageNet-1k evaluations.

AINeutralarXiv – CS AI · 4d ago6/10

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AnchorDiff: Training-Free Concept Grounding for MM-DiTs via Anchor-Based Graph Propagation

Researchers propose AnchorDiff, a training-free method for improving concept grounding in Multi-Modal Diffusion Transformers by addressing 'concept leakage' where attention activations overlap on visually similar objects. The approach uses anchor-based graph propagation to better localize and distinguish between confusable concepts, with evaluation on a newly introduced Multi-Concept Confusion Dataset.