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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/frederick0329/text-classification-benchmark

Convolutional Neural Networks for Sentence Classification

https://github.com/fshamshirdar/pytorch-rdpg

Continuous control with deep reinforcement learning

https://github.com/fstahlberg/tensor2tensor

Attention Is All You Need

https://github.com/funaudiollm/cv3-eval

CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training

https://github.com/g-eklund/bert_tutorial

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

https://github.com/ganeshjawahar/interpret_bert

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

https://github.com/gasserelbanna/serab-byols

SERAB: A multi-lingual benchmark for speech emotion recognition

https://github.com/gaussic/text-classification-cnn-rnn

Convolutional Neural Networks for Sentence Classification

https://github.com/gavincaoyuji/NLP

Attention Is All You Need

https://github.com/gbcolborne/lang_id

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

https://github.com/gcervantes8/Game-Image-Generator

Large Scale GAN Training for High Fidelity Natural Image Synthesis

https://github.com/gcunhase/embracebert

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

https://github.com/geekyutao/inpaint-anything

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/gengshan-y/high-res-stereo

Hierarchical Deep Stereo Matching on High-resolution Images

https://github.com/geondopark/ckd

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

https://github.com/georgkruse/cleanqrl

Continuous control with deep reinforcement learning

https://github.com/gggggeun/DeepLearning_Architecture

Attention Is All You Need

https://github.com/ghliu/pytorch-ddpg

Continuous control with deep reinforcement learning

https://github.com/giagia96/DeepComedy

Attention Is All You Need

https://github.com/glarbi/Face-detection

Selective Refinement Network for High Performance Face Detection

https://github.com/glassroom/heinsen_routing

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

https://github.com/glouppe/info8010-deep-learning

Attention Is All You Need

https://github.com/gmichalo/question_identification_on_medical_logs

Convolutional Neural Networks for Sentence Classification

https://github.com/gmum/dl-mo-2021

Attention Is All You Need

https://github.com/going-doer/paper2code

Attention Is All You Need

https://github.com/goldenbili/Bert_Test2

Attention Is All You Need

https://github.com/goldenbili/Bert_Test3

Attention Is All You Need

https://github.com/goldenbili/bert_lamb_pretrain

Attention Is All You Need

https://github.com/gongzix/neuroclips

Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

https://github.com/goodluck4s/bert-

Attention Is All You Need

https://github.com/google-deepmind/jax_privacy

Unlocking High-Accuracy Differentially Private Image Classification through Scale

https://github.com/google-deepmind/xquad

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

https://github.com/google-research/bigbird

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

https://github.com/google-research/tensorflow_constrained_optimization

Two-Player Games for Efficient Non-Convex Constrained Optimization

https://github.com/google/flax/tree/master/examples/wmt

Attention Is All You Need

https://github.com/google/seqio

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.