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/EverLookNeverSee/fer_tl_dcnn

https://github.com/EyaRhouma/collaboration-competition-MADDPG

Continuous control with deep reinforcement learning

https://github.com/FaceOnLive/DeepFake-Detection-SDK-Linux

Taming Transformers for High-Resolution Image Synthesis

https://github.com/FaceOnLive/License-Plate-Recognition-SDK-Android

Vehicle and License Plate Recognition with Novel Dataset for Toll Collection

https://github.com/Farahn/AES

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

https://github.com/FaskyCC/TextClassification

Attention Is All You Need

https://github.com/FengJiaChunFromSYSU/Bert

Attention Is All You Need

https://github.com/FengJiaChunFromSYSU/fastText

Bag of Tricks for Efficient Text Classification

https://github.com/FilippoAiraldi/global-optimization

PyTorch: An Imperative Style, High-Performance Deep Learning Library

https://github.com/FlorianPfisterer/2D-LSTM-Seq2Seq

Attention Is All You Need

https://github.com/FlyienSHaDOw/continuous_control

Continuous control with deep reinforcement learning

https://github.com/FlyienSHaDOw/project_2_continuous_control

Continuous control with deep reinforcement learning

https://github.com/Francis-Hsu/alocv

Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions

https://github.com/Francis-Rings/MotionEditor

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/Francis-Rings/MotionFollower

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/Fuchai/language

Attention Is All You Need

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction

Attention Is All You Need

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold

Attention Is All You Need

https://github.com/GGiecold/Cluster-Ensembles

Robust Lineage Reconstruction from High-Dimensional Single-Cell Data

https://github.com/GGiecold/Cluster_Ensembles

Robust Lineage Reconstruction from High-Dimensional Single-Cell Data

https://github.com/Gaozhen0816/BERT_QA_For_AILaw

Attention Is All You Need

https://github.com/Gaozhen0816/BERT_QA_for_Chinese

Attention Is All You Need

https://github.com/Garfield35/Doodle

Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks

https://github.com/GarrickZ2/Image-Denoising

Restormer: Efficient Transformer for High-Resolution Image Restoration

https://github.com/GauthierDmn/question_answering

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

https://github.com/GerardLiu96/FECNet

A Compact Embedding for Facial Expression Similarity

https://github.com/GitHubSprint/fasttext4j

Bag of Tricks for Efficient Text Classification

https://github.com/Glaceon31/Document-Transformer

Attention Is All You Need

https://github.com/GlassyWing/transformer-keras

Attention Is All You Need

https://github.com/GokulKarthik/MagNet.pytorch

MagNet: a Two-Pronged Defense against Adversarial Examples

https://github.com/Gouet/DDPG_PendulumV1

Continuous control with deep reinforcement learning

https://github.com/Gpanayu/EmoRecogKeras

Spatiotemporal Emotion Recognition using Deep CNN Based on EEG during Music Listening

https://github.com/GrHound/imagemorph.c

Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)

https://github.com/GregDobby/Multi-Fidelity-High-Order-Gaussian-Processes-for-Physical-Simulation

Multi-Fidelity High-Order Gaussian Processes for Physical Simulation

https://github.com/GuangyanZhang/SCNN-Deeplabv3-bisenet-icnet

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

https://github.com/Guowei-Yan/Data

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

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