YuanWanglll's repositories
focal_loss_pytorch
A PyTorch Implementation of Focal Loss.
faster-rcnn.pytorch
A faster pytorch implementation of faster r-cnn
dlcv_for_beginners
《深度学习与计算机视觉》配套代码
class-balanced-loss
Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019
C3D
C3D is a modified version of BVLC caffe to support 3D ConvNets.
GHM_Detection
The implementation of “Gradient Harmonized Single-stage Detector” published on AAAI 2019.
few-shot-ssl-public
Meta Learning for Semi-Supervised Few-Shot Classification
fewshot-egnn
Edge-labeling Graph Neural Network for Few-shot Learning
ScratchDet
The code and models for paper: "ScratchDet: Exploring to Train Single-Shot Object Detectors from Scratch"
NIST-FSD
NIST-FSD: a benchmark for few-shot object detection.
LGM-Net
Tensorflow code for ICML 2019 paper: LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
MAML-Pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
TPN
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.
gan
some demo of GANs
leo
Implementation of Meta-Learning with Latent Embedding Optimization
DSS
code for "Deeply supervised salient object detection with short connections" published in CVPR 2017
PoolNet
Code for our CVPR 2019 paper "A Simple Pooling-Based Design for Real-Time Salient Object Detection"
DSS-pytorch
:star: PyTorch implement of Deeply Supervised Salient Object Detection with Short Connection
few-shot-gnn
FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS
TADAM
The implementation of https://papers.nips.cc/paper/7352-tadam-task-dependent-adaptive-metric-for-improved-few-shot-learning
learning-to-learn-by-pytorch
Learning to learn by gradient descent by gradient descent
simple-faster-rcnn-pytorch
A simplified implemention of Faster R-CNN that replicate performance from origin paper
learning-to-reweight-examples
Code for paper "Learning to Reweight Examples for Robust Deep Learning"
ANIML
Reproduction of "Model-Agnostic Meta-Learning" (MAML) and "Reptile".
LearningToCompare_FSL
Learning to Compare: Relation Network for Few-Shot Learning
DAOSL
Implementation of Domain Adaption in One-Shot Learning
maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
meta-learning-lstm-pytorch
Optimization as a Model for Few-shot Learning