sunshine's starred repositories
Swin-Transformer
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
HHAR-Data-Process
Code for data processing used for the experiment in paper "Deepsense: a unified deep learning framework for time-series mobile sensing data processing"
BottleneckTransformers
Bottleneck Transformers for Visual Recognition
Efficient-AI-Backbones
Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
CMT_CNN-meet-Vision-Transformer
A PyTorch implementation of CMT based on paper CMT: Convolutional Neural Networks Meet Vision Transformers.
CMT-pytorch
CMT: Convolutional Neural Networks Meet Vision Transformers
strangan-chase-2021
PyTorch Implementation of IEEE/ACM CHASE 2021 paper "STranGAN: Adversarially-Learnt Spatial Transformer for Scalable Human Activity Recognition"
PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
improved_wgan_training
Code for reproducing experiments in "Improved Training of Wasserstein GANs"
DeepImage-an-Image-to-Image-technology
DeepNude's algorithm and general image generation theory and practice research, including pix2pix, CycleGAN, UGATIT, DCGAN, SinGAN, ALAE, mGANprior, StarGAN-v2 and VAE models (TensorFlow2 implementation). DeepNude的算法以及通用生成对抗网络（GAN,Generative Adversarial Network）图像生成的理论与实践研究。
AdversarialNetsPapers
Awesome paper list with code about generative adversarial nets
Baseline-with-HAR-datasets
3-layer-CNN and ResNet with OPPORTUNITY dataset, PAMAP2 dataset, UCI-HAR dataset, UniMiB-SHAR dataset, USC-HAD dataset, and WISDM dataset.
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
pytorch-image-models
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
continual-learning-for-HAR
A continual model for human activity recognition (called HAR-GAN). It is based on a technique called `generative replay` which two models (classifier and generator) are trainined in the same time to keep learning a new task but also retrain previously learned knwoledge.
gansformer
Generative Adversarial Transformers
Self-Attention-GAN
Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)
Human-Activity-Recognition-Using-Wearable-Devices
Publications, codes, and datasets related to our work on human activity recognition using IMU sensors embedded on wearable devices
Human_Activity_Recognition_using_Wearable_Sensors_Review_Challenges_Evaluation_Benchmark
This github is an implementation for accepted manuscript titled Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark.
WearableSensorData
This repository provides the codes and data used in our paper "Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art", where we implement and evaluate several state-of-the-art approaches, ranging from handcrafted-based methods to convolutional neural networks.
motion-sense
MotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope) (PMC Journal) (IoTDI'19)
har-with-imu-transformer
Intertial-based Human Activity Recognition with Transformers
center-loss.pytorch
center loss for face recognition