Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟱𝟲 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗕𝗲𝗶𝗷𝗶𝗻𝗴 𝗡𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗖𝗲𝗻𝘁𝗲𝗿 𝗳𝗼𝗿 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗥𝗲𝗽𝗠𝗟𝗣𝗡𝗲𝘁: 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗩𝗶𝘀𝗶𝗼𝗻 𝗠𝗟𝗣 𝘄𝗶𝘁𝗵 𝗥𝗲-𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝗶𝘇𝗲𝗱 𝗟𝗼𝗰𝗮𝗹𝗶𝘁𝘆 Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗥𝗲𝗽𝗠𝗟𝗣𝗡𝗲𝘁: 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗩𝗶𝘀𝗶𝗼𝗻 𝗠𝗟𝗣 𝘄𝗶𝘁𝗵 𝗥𝗲-𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝗶𝘇𝗲𝗱 𝗟𝗼𝗰𝗮𝗹𝗶𝘁𝘆 🔸 This paper is published arxiv2021. 🔸 This paper proposes a re-parameterization methodology to inject locality into FC layers, a novel MLP-style block, and a hierarchical MLP architecture. The proposed RepMLPNet is favorable compared to several concurrently proposed MLP architectures in terms of the accuracyefficiency trade-off and the training costs. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Compared to convolutional layers, fully-connected (FC) layers are better at modeling the long-range dependencies but worse at capturing the local patterns, hence usually less favored for image recognition.  🔸 In this paper, we propose a methodology, Locality Injection, to incorporate local priors into an FC layer via merging the trained parameters of a parallel conv kernel into the FC kernel.  🔸 Locality Injection can be viewed as a novel Structural Re-parameterization method since it equivalently converts the structures via transforming the parameters.  🔸 Based on that, we propose a multi-layer-perceptron (MLP) block named RepMLP Block, which uses three FC layers to extract features, and a novel architecture named RepMLPNet.  🔸 The hierarchical design distinguishes RepMLPNet from the other concurrently proposed vision MLPs. As it produces feature maps of different levels, it qualifies as a backbone model for downstream tasks like semantic segmentation. Our results reveal that  1️⃣ Locality Injection is a general methodology for MLP models;  2️⃣RepMLPNet has favorable accuracy-efficiency trade-off compared to the other MLPs;  3️⃣ RepMLPNet is the first MLP that seamlessly transfer to Cityscapes semantic segmentation.  #computervision #artificialintelligence #innovation