panbo-bridge Goto Github PK

arl-eegmodels

This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

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。

chinesenlpcorpus

搜集、整理、发布 中文 自然语言处理 语料/数据集，与 有志之士 共同 促进 中文 自然语言处理 的 发展。

competition

comprehensive-analysis-of-deep-learning-for-electroencephalogram-eeg-classification-tasks

EEG emotion classification using CNN

dbpn-pytorch

The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)

de_cnn

This repository contains the tensorflow implementation for our ICONIP-2018 paper: "Continuous Convolutional Neural Network with 3D Input for EEG-Based Emotion Recognition" (To appear...)

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

PyTorch EEG emotion analysis using DEAP dataset

eeg-emotion-recognition

This is a final project for my 2019 Winter course "Pattern Recognition".

eeg-emotion-recognition-base-gan-and-cnn

eeg_emotion_classifier_deap

EEG Emotion classification using the DEAP pre-processed data

emorecogkeras

This repository is a part of EEG-Emotion Recognition Research. It manifests models used in our experiments.

emotion-classification-in-eeg-signals

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

github_peterl1n_backgroundmattingv2

high-fidelity-generative-compression

Pytorch implementation of High-Fidelity Generative Image Compression + Routines for neural image compression

image_processing_for_mold_damage_detection

Machine Learning and OpenCV based approaches for detecting surface defects in casting products

imagecompression

This is the codes for paper "Learning Convolutional Networks for Content-weighted Image Compression"

lossless-image-compression

利用python的Image库对图片进行无损压缩

lstm4imagecompression

Graduation Project: Image Compression based on LSTM-pytorch implement

luban-py

Python version of Luban(鲁班)—Image compression with efficiency very close to WeChat Moments/可能是最接近微信朋友圈的图片压缩算法