Avishek Nandi's Projects
AI Roadmap:机器学习(Machine Learning)、深度学习(Deep Learning)、对抗神经网络(GAN),图神经网络(GNN),NLP,大数据相关的发展路书(roadmap), 并附海量源码(python,pytorch)带大家消化基本知识点,突破面试,完成从新手到合格工程师的跨越,其中深度学习相关论文附有tensorflow caffe官方源码,应用部分含推荐算法和知识图谱
ARM - Official PyTorch Implementation
Config files for my GitHub profile.
Top conferences & Journals focused on Facial expression recognition (FER)/ Facial action unit (FAU)
Paper Lists for Graph Neural Networks
🤓 Build your own (insert technology here)
source code to ICLR'19, 'A Closer Look at Few-shot Classification'
A complete computer science study plan to become a software engineer.
Covariance Pooling for Facial Expression Recognition
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)
Deriving and classifying basic human emotion from the face of a person.
Pytorch implementation of "Real-time Convolutional Neural Networks for Emotion and Gender Classification" (mini-Xception)
Efficient face emotion recognition in photos and videos
Facial Expression Recognition Using Siamese Network
Facial Expression Recognition with CNNs on TensorFlow-Keras with OpenCV and Python.
A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset
Implementing Siamese networks with a contrastive loss for similarity learning
Facial Expression Recognition with a deep neural network as a PyPI package
Challenges in Representation Learning: Facial Expression Recognition Challenge
This is the FER+ new label annotations for the Emotion FER dataset.
Repository for few-shot learning machine learning projects
Sample Code for Gated Graph Neural Networks
Must-read papers on graph neural networks (GNN)
GPT-3: Language Models are Few-Shot Learners
Build Graph Nets in Tensorflow
Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.
PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)