bopan's repositories
Comprehensive-analysis-of-Deep-learning-for-electroencephalogram-EEG-classification-tasks
EEG emotion classification using CNN
image_processing_for_mold_damage_detection
Machine Learning and OpenCV based approaches for detecting surface defects in casting products
BackgroundMattingV2
Real-Time High-Resolution Background Matting
Chinese-Text-Classification-Pytorch
中文文本分类,TextCNN,TextRNN,FastText,TextRCNN,BiLSTM_Attention,DPCNN,Transformer,基于pytorch,开箱即用。
DBPN-Pytorch
The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)
Deep-Residual-Shrinkage-Networks
The deep residual shrinkage network is a variant of deep residual networks.
DeepLearningSlideCaptcha
Deep Learning Slide Captcha
DRN
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
ECE-C247-EEG-GAN
GAN and VAE implementations to generate artificial EEG data to improve motor imagery classification. Data based on BCI Competition IV, datasets 2a. Final project for UCLA's EE C247: Neural Networks and Deep Learning course.
EEG-Emotion-Recognition
This is a final project for my 2019 Winter course "Pattern Recognition".
EmoRecogKeras
This repository is a part of EEG-Emotion Recognition Research. It manifests models used in our experiments.
Emotion-Recognition-based-on-EEG-using-Generative-Adversarial-Nets-and-Convolutional-Neural-Network
Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications.Currently, deep learning has been widely used in the field of EEG emotion recognition and has achieved remarkable results. However, due to the small amount of EEG data and the serious imbalance in the proportion of EEG data categories, it is difficult to use deeper models. In addition, we believe that there is a frequency band correlation feature between the EEG signal frequency bands, which has an important effect on EEG emotion recognition. In this paper, we first proposed an adversarial neural network model for sample generation. Because we used PSD features in the experiment, this generative model is called PSD-GAN. Then we designed FBSCNN(Frequency band separation convolutional neural network) and FBCCNN(Frequency Band Correlation Convolutional Neural Network) models as a comparison to explore the influence of frequency band correlation features on EEG emotion recognition. Among them, FBSCNN can not extract the frequency band correlation features, but FBCCNN can extract the frequency band correlation features. The experimental results show that the samples generated by PSD-GAN have good performance, and the frequency band correlation feature can effectively improve the accuracy of EEG emotion recognition. Moreover, we compare our FBCCNN + PSD-GAN model with similar studies and the results show that our model is highly competitive.
Emotion-Recognition-from-brain-EEG-signals-
Emotion recognition can be achieved by obtaining signals from the brain by EEG . This test records the activity of the brain in form of waves. We have used DEAP dataset on which we are classifying the emotion as valance, likeness/dislike, arousal, dominance. We have used LSTM and CNN classifier which gives 88.60 % accuracy to predict the model successfully.
GAIIC2023
GAIIC赛道一:影像学 NLP — 医学影像诊断报告生成 [A100换你大棚甜瓜 Rank-12 方案]
Gated-Transformer-on-MTS-1
使用改良的Transformer模型应用于多维时间序列的分类任务上
generative-compression
TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression
high-fidelity-generative-compression
Pytorch implementation of High-Fidelity Generative Image Compression + Routines for neural image compression
PaddleGAN
PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, wav2lip, picture repair, image editing, photo2cartoon, image style transfer, and so on.
Pytorch_LongText_Classification_Demo
Pytorch进行长文本分类。这里用到的网络有:FastText、TextCNN、TextRNN、TextRCNN、Transformer
slider-unlock
基于opencv图像识别的滑块验证码破解Demo
SReC
PyTorch Implementation of "Lossless Image Compression through Super-Resolution"
Tianchi-2021-Guangdong-Tile-Detection
天池2021广东工业智能制造大赛瓷砖瑕疵检测极客奖方案
USRNet
Deep Unfolding Network for Image Super-Resolution (CVPR, 2020) (PyTorch)