tht106 Goto Github PK
Name: HT
Type: User
Company: uOttawa
Bio: I am interested in unsupervised transfer learning models, such as domain adaptation and meta-learning, that are devised for CNN/GCN based computer vision tasks.
Location: ottawa
Name: HT
Type: User
Company: uOttawa
Bio: I am interested in unsupervised transfer learning models, such as domain adaptation and meta-learning, that are devised for CNN/GCN based computer vision tasks.
Location: ottawa
README and scripts for the Cityscapes Dataset
( CVPR2019 Oral ) Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
Domain Agnostic Normalization layer for Unsupervised Domain Adaptation
Deep Residual Learning for Image Recognition
Datasets and code for reproducing results for IV2018 paper "Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation".
OpenMMLab Detection Toolbox and Benchmark
Pytorch implementation of convolutional neural network visualization techniques
Semi-Supervised Adaptation for Skeletal Data Based Human Action Recognition
SSD: Single Shot MultiBox Detector | a PyTorch Model for Object Detection | VOC , COCO | Custom Object Detection
Everything about Transfer Learning and Domain Adaptation--迁移学习
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