Ashish Patel 🇮🇳’s Post

Day-74 Computer Vision Learning DeepLabv3+ — Atrous Separable Convolution (Semantic Segmentation) by Google Follow me for similar post : 🇮🇳 Ashish Patel Interesting Facts : 🔸 It is published in 2018 ECCV, which has already got over 3130 citations. 🔸 DeepLab series has come along for versions from DeepLabv1 (2015 ICLR), DeepLabv2 (2018 TPAMI), and DeepLabv3 (arXiv). 🔸 Outperforms LC, ResNet-DUC-HDC, GCN, RefineNet, ResNet-38, PSPNet, IDW-CNN, SDN, DIS, and DeepLabv3 ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://bit.ly/3lhmsny Official Code : https://lnkd.in/eNbrAyC tensorflow : https://bit.ly/2Nm1Tda pytorch : https://bit.ly/3qLCXcM keras : https://bit.ly/3ll88uq ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 (a): With Atrous Spatial Pyramid Pooling (ASPP), able to encode multi-scale contextual information. 🔸(b): With Encoder-Decoder Architecture, the location/spatial information is recovered. Encoder-Decoder Architecture has been proved to be useful in literature such as FPN, DSSD, TDM, SharpMask, RED-Net, and U-Net for different kinds of purposes. 🔸 (c): DeepLabv3+ makes use of (a) and (b). #computervision #artificialintelligence #analytics