Beast code in Giters

PlumedSerpent's repositories

Adaptively-Parametric-RELU-pytorch

An unofficial implementation of （Adaptively Parametric Rectifier Linear Unit，APReLU）.

Language:Python11 10

ASM_pytorch

An unofficial PyTorch implementation of ASM: Adversarial Structure Matching for Structured Prediction Tasks in CVPR2019.

MIT010

CM-Erase-REG

Code for CVPR 19 Paper "Improving Referring Expression Grounding with Cross-modal Attention-guided Erasing"

Language:Python000

Conformer

Official code for Conformer: Local Features Coupling Global Representations for Visual Recognition

Language:Jupyter NotebookApache-2.0000

dbscan_python

用python实现的DBSCAN聚类(一种基于密度的聚类算法)，并且将分布打印在二维平面

Language:Python000

deep-learning-v2-pytorch

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

Language:Jupyter NotebookMIT000

FIDTM

Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd

Language:PythonMIT000

Here is the implementation of Maxout Layer from paper: https://arxiv.org/pdf/1302.4389.pdf in PyTorch. In forward pass, maxout for the feature maps of CNN is calculated. For backward pass, derivative of error w.r.t input is only propagated through the cells of feature maps array which were activated in forward pass(which is derivative of Maxout). To be sure this works fine, results were compared with Maxout in Theano(which is with the name featurepool layer) and were confirmed to be working fine. To make it work according to your desire, just change max_out parameter in forward function. The code is very efficient and fast.

Language:Jupyter Notebook010