AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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An Uncertainty-guided Adverse-weather Restoration Network (UAR-Net), a weather-specific AiO framework that integrates a gated transformer with balanced multi-scale skip connections that achieves state-of-the-art performance across multiple adverse-weather benchmarks.
This paper proposes AdaptIR, an efficient all-in-one image restoration network equipped with adaptive frequency enhancement, which achieves state-of-the-art performance on all-in-one image restoration tasks involving multiple degradations, while maintaining high computational efficiency.
Backbone-Agnostic Stochastic Perturbation Learning (BSPL) is proposed, a plug-and-play framework for end-to-end real-world image dehazing and a Dual-space Domain-diversified Distribution-aware Contrastive Loss to regularize both clean restoration and hazy reconstruction spaces with real-world and synthetic negatives.
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