Yi-Chen (Howard) Lo's repositories
papernotes
My personal notes and surveys on DL, CV and NLP papers.
pytorch-seq2seq-example
Fully batched seq2seq example based on practical-pytorch, and more extra features.
CLCC-CVPR21
An official TensorFlow implementation of “CLCC: Contrastive Learning for Color Constancy” accepted at CVPR 2021.
Kaggle-Quora-Question-Pairs
This is our team's solution report, which achieves top 10% (305/3307) in this competition.
grammar-pattern
Extract and align grammar patterns from English sentences.
NTHU-Machine-Learning
NTHU EE6550 Machine Learning slides and my code solutions for spring semester 2017.
ImageNet2COCO
A demo for mapping class labels from ImageNet to COCO.
NTHU-Computer-Vision
NTHU CS6550 Computer Vision slides and my code solutions for fall semester 2016.
NTHU-CEDL2017-HW1-Deep-Classfication
TensorFlow implementation of two-stream VGG-16 net for object classfication in video frames
OpenNMT-py
Open Source Neural Machine Translation in PyTorch
synthetic-chart
Code to generate style-enriched chart images and annotations based on Matplotlib and Seaborn.
awesome-adversarial-machine-learning
A curated list of awesome adversarial machine learning resources
awesome-meta-learning
A curated list of Meta-Learning resources/papers.
awesome-zero-shot-learning
A curated list of papers, code and resources pertaining to zero shot learning
awsome-domain-adaptation
A collection of AWESOME things about domian adaptation
BalancedMSE
[CVPR 2022 Oral] Balanced MSE for Imbalanced Visual Regression
disentangled-representation-papers
A curated list of research papers related to learning disentangled representations
Domain-generalization
All about domain generalization
ml-contests-conf
ML and DL related contests, competitions and conference challenges.
NTHU-CEDL2017-HW2-MDPs
The homework for Cutting-Edge of Deep Learning, aka CEDL, from NTHU
NTHU-CEDL2017-HW3-Policy-Gradient
The homework for Cutting-Edge of Deep Learning, aka CEDL, from NTHU
One-Shot-Object-Detection
Implementation of One-Shot Object Detection with Co-Attention and Co-Excitation in Pytorch