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/MOHAMEDELDAKDOUKY/bert_adjusted

Attention Is All You Need

https://github.com/MS-Mind/MS-Code-01/tree/main/soft_masked_bert

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

https://github.com/MS-Mind/MS-Code-06/tree/main/NFNet

High-Performance Large-Scale Image Recognition Without Normalization

https://github.com/MS-P3/code5/tree/main/bert

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

https://github.com/MaZhiyuanBUAA/bert-tf1.4.0

Attention Is All You Need

https://github.com/MagnusXu/Speech-Emotion-Recognition-Capstone-Project

Multimodal Speech Emotion Recognition Using Audio and Text

https://github.com/MahyarFardin/Monocular_depth_estimation_via_transfer_learning

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/MalteHB/-l-ctra

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

https://github.com/ManasRMohanty/DS5500-capstone

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

https://github.com/MansMeg/IntroML

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

https://github.com/ManuelVs/NNForTextClassification

Convolutional Neural Networks for Sentence Classification

https://github.com/ManuelVs/NeuralNetworks

Convolutional Neural Networks for Sentence Classification

https://github.com/Maple728/transformer

Attention Is All You Need

https://github.com/MarcBS/keras

Attention Is All You Need

https://github.com/MarcosGrzeca/drunktweets

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

https://github.com/MartinSpindler/hdm

High-Dimensional Metrics in R

https://github.com/Mary-xl/HRnet_Kaggle_iNat2019_FGVC

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/MashaKubyshina/learning_ML

Attention Is All You Need

https://github.com/MathPhysSim/PER-NAF

Continuous control with deep reinforcement learning

https://github.com/MatthewSwahn/news-headlines-sarcasm-detector

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

https://github.com/Matthewdowney18/Transformer_Dialogue

Attention Is All You Need

https://github.com/MaxLikesMath/DeepLearningImplementations

DeXpression: Deep Convolutional Neural Network for Expression Recognition

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/FastNeRF_High_Fidelity_Neural_Rendering_at_200FPS

FastNeRF: High-Fidelity Neural Rendering at 200FPS

https://github.com/MayukhSobo/Conv4RNN

Convolutional Neural Networks for Sentence Classification

https://github.com/Maz101/Bert

Attention Is All You Need

https://github.com/Maz101/pretrainingdataset

Attention Is All You Need

https://github.com/McDo/LightFlowPytorch

Towards High Performance Video Object Detection for Mobiles

https://github.com/Medabid1/RL_Project

Continuous control with deep reinforcement learning

https://github.com/Mengyanz/Mengyanz.github.io

Two-Stage Neural Contextual Bandits for Personalised News Recommendation

https://github.com/MhYao2014/HyperbolicEmbedding

Bag of Tricks for Efficient Text Classification

https://github.com/MichaelZhouwang/LMlexsub

Attention Is All You Need

https://github.com/Michaelgod/test

Two-Stream Convolutional Networks for Action Recognition in Videos

https://github.com/MichauGabriel/HELM

Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals

https://github.com/MicroprocessorX069/Comparison-of-DC-GANS-and-SA-GANS

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/Microsoft/AzureML-BERT

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

https://github.com/Mind23-2/MindCode-101/tree/main/HRNetW48_cls

Deep High-Resolution Representation Learning for Visual Recognition

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