WangChaofeng

Awesome-Super-Resolution. Collect super-resolution related papers, data, repositories

AWSRN. PyTorch code for our paper "Lightweight Image Super-Resolution with Adaptive Weighted Learning Network"

Npair_loss_pytorch. Improved Deep Metric Learning with Multi-class N-pair Loss Objective

L-GM_loss_pytorch. Rethinking Feature Distribution for Loss Functions in Image Classification

DeepLearning-500-questions. 深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为15个章节，近20万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06

Paper_Reading_List. Recommended Papers. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Learning (cs.LG)

caffe. Caffe on both Linux and Windows

MobileSAM. This is the offiicial code for Faster Segment Anything (MobileSAM) project that makes SAM lightweight

netscope. Neural network visualizer

bcnn. MATLAB