Papers with Code 아카이브

Emotion Recognition

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하위 태스크: 12
평가표: 10
보존된 평가 기록: 18

Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/pisalore/roberta_results

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/piyush2896/CNN-Text-Classifier

Convolutional Neural Networks for Sentence Classification

https://github.com/piyushpandey615/GAN-based-Image-Compressor-System

High-Fidelity Generative Image Compression

https://github.com/plaidml/tpp-mlir

High-Performance Deep Learning via a Single Building Block

https://github.com/pliang279/MFN

Multi-attention Recurrent Network for Human Communication Comprehension

https://github.com/plkmo/Transformer-Eng2French

Attention Is All You Need

https://github.com/plkumjorn/FIND

Convolutional Neural Networks for Sentence Classification

https://github.com/pluflou/MLreviewNotebooks

A high-bias, low-variance introduction to Machine Learning for physicists

https://github.com/pohlinwei/AComPianist

Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark

https://github.com/polzinben/TensorFlow_NLP

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/pommedeterresautee/fastrtext

Bag of Tricks for Efficient Text Classification

https://github.com/pomonam/AttentionCluster

Attention Is All You Need

https://github.com/pooruss/ICNet-Paddle2.2.0rc

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

https://github.com/ppke-nlpg/fastText_factored-cbow

Bag of Tricks for Efficient Text Classification

https://github.com/ppocma/ppocma

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/prabhakar267/LASER-improved

CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB

https://github.com/prabhupad26/100daysofML

Attention Is All You Need

https://github.com/prajwalgatti/DRL-Collaboration-and-Competition

Continuous control with deep reinforcement learning

https://github.com/prajwalgatti/DRL-Continuous-Control

Continuous control with deep reinforcement learning

https://github.com/prakashSidd18/blind_augmentation

Restormer: Efficient Transformer for High-Resolution Image Restoration

https://github.com/prakashpandey9/Text-Classification-Pytorch

Convolutional Neural Networks for Sentence Classification

https://github.com/pranjalg96/Stylized-Image-captioning

Convolutional Neural Networks for Sentence Classification

https://github.com/pranshupant/SR-DNS_Net

Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data

https://github.com/pridiltal/stray_manuscript

Anomaly Detection in High Dimensional Data

https://github.com/princefr/Light-Head.pytorch

Light-Head R-CNN: In Defense of Two-Stage Object Detector

https://github.com/pritam1322/Machine-Translation-with-Transformer

Attention Is All You Need

https://github.com/prithvi2226/Bilingual-Sentiment-Analysis

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

https://github.com/priya22/emotion-granularity

Ethics Sheets for AI Tasks

https://github.com/priya22/emotion-granularity-from-text

Ethics Sheets for AI Tasks

https://github.com/priya22/emotiondynamics

Ethics Sheets for AI Tasks

https://github.com/ps2811/Bert-Multi-Label-Text-Classification-GitVersion

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/psh01087/simple-nfnet

High-Performance Large-Scale Image Recognition Without Normalization

https://github.com/pwc-1/Paper-8/tree/main/audio_spectrogram_transformer

AST: Audio Spectrogram Transformer

https://github.com/pwc-1/Paper-9/tree/main/4/Translation-Invariant/model

Attention Is All You Need

https://github.com/pwc-1/Paper-9/tree/main/4/Two-Layer-ReLU-Network-Analytically

Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent

https://github.com/pwc-1/Paper-9/tree/main/7/RGNN

EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.