HonFii's repositories
AdversarialNetsPapers
The classical paper list with code about generative adversarial nets
bag-of-local-features-models
Pretrained bag-of-local-features neural networks
CapsNet-Tensorflow
A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules
Faster-RCNN-TensorFlow-Python3
Tensorflow Faster R-CNN for Windows/Linux and Python 3 (3.5/3.6/3.7)
few_shot_learning
classical model code implementation of few-shot/one-shot lenaring, including siamese network, prototypical network, relation network, induction network
FREE-VPN-1
我自己分享的一些免费机场以及免费VPN，白嫖为主
Hands-On-Meta-Learning-With-Python
Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
HonFii.github.io
A minimal Jekyll Theme to host your resume (CV)
Keras-FewShotLearning
Some State-of-the-Art few shot learning algorithms in tensorflow 2
keras-oneshot
koch et al, Siamese Networks for one-shot learning, (mostly) reimplimented in keras
kuka-rsi-ros-interface
A ROS node for the manipulation of a KUKA robot arm via RSI 3
LearningToCompare_FSL
PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)
machinelearning
My blogs and code for machine learning. http://cnblogs.com/pinard
MAML_Pytorch
MAML implementation with pytorch
Meta-Learning-Papers
Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning
meta-transfer-learning
TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)
opencv3-python-learn
learning notes
PlotNeuralNet
Latex code for making neural networks diagrams
Prototypical-Networks-for-Few-shot-Learning-PyTorch
Implementation of Prototypical Networks for Few Shot Learning (https://arxiv.org/abs/1703.05175) in Pytorch
pseudo-shots
Pseudo Shots: Few-Shot Learning with Auxiliary Data
robotics-carpuzzle
Robotics Module, Master Course IT-Engineering, FH Wedel
shadowsocks-android
A shadowsocks client for Android
SRGAN-tensorflow
Tensorflow implementation of the SRGAN algorithm for single image super-resolution