2024年1月24日 · Building upon this foundation, we propose an innovative approach, termed the Unified-Width Adaptive Dynamic Network (U-WADN), consisting of two pivotal components: a Width Adaptive Backbone (WAB) and a Width Selector (WS).

Width Adaptive Network (U-WADN) is tailored to address more complex all-in-one image restoration challenges in-volving multiple degradations. U-WADN goes beyond as-sessing just sample-wise difficulties; it also evaluates the intricacies of each restoration task, enabling more effective computational resource allocation.

Building upon this foundation, we propose an innovative approach, termed the Unified-Width Adaptive Dynamic Network (U-WADN), consisting of two pivotal components: a Width Adaptive Backbone (WAB) and a Width Selector (WS).

Wasserstein Aggregation Domain Network A pytorch implementation of Aggregating From Multiple Target-Shifted Sources. title = {Aggregating From Multiple Target-Shifted Sources}, author = {Shui, Changjian and Li, Zijian and Li, Jiaqi and Gagn{\'e}, Christian and Ling, Charles X and Wang, Boyu},

In our study, we introduce a Unified-Width-Adaptive Dynamic Network (U-WADN) designed for all-in-one image restoration tasks. This network comprises a Width-Adaptive Backbone (WAB) and a Width-Selector (WS).

2024年1月24日 · Building upon this foundation, we propose an innovative approach, termed the Unified-Width Adaptive Dynamic Network (U-WADN), consisting of two pivotal components: a Width Adaptive Backbone (WAB) and a Width Selector (WS).

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2011年3月8日 · PyTorch implementation for Unified-Width Adaptive Dynamic Network for All-In-One Image Restoration (U-WADN). You could find the dataset we used in the paper at following: Denoising: BSD400, WED, Urban100. Deraining: Train100L&Rain100L. Dehazing: RESIDE (OTS) The pretrained model is upload in ckpt/best_ckpt/best.pth.

2024年1月24日 · This work introduces a novel concept positing that intricate image degradation can be represented in terms of elementary degradation, and proposes an innovative approach, termed the Unified-Width Adaptive Dynamic Network (U-WADN), consisting of two pivotal components: a Width Adaptive Backbone (WAB) and a Width Selector (WS).

本文是Google 2013年在KDD上发表 论文 的笔记，这是一篇将已有的学术研究成果进行大规模实践检测其是否有效的 论文，是一篇实用性大于研究性的 论文，相信偏向机器学习应用的同学能从这篇 论文 中受益匪浅，特将此 论文 进行总结，记录笔记，以供后面使用的时候查询。 这篇 论文 的博客写了很久才写完，一方面由于之前实习加找工作实在太忙，另一方面，这篇 论文 中中的干货太多，所以下方高能。 今天终于写完了，这也是我一贯奉行的原则，即便走的.