Papers with Code 아카이브
Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/KnightZhang625/BERT_TF
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/KomorebiLHX/Emotion-Recognition-in-Conversations
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Kosuke-Szk/BERT-NER-ja
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Koukyosyumei/AIJack
https://github.com/KrishnaTarun/Deep-Learning-Lab
https://github.com/KunZhou9646/controllable_evc_code
Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset
https://github.com/Kyubyong/bert-token-embeddings
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Kyubyong/transformer
https://github.com/LM095/DDPG-implementation
https://github.com/LeBenchmark/Interspeech2021
https://github.com/LedererLab/tridge
Tuning-free ridge estimators for high-dimensional generalized linear models
https://github.com/Leminhbinh0209/AAAI22-ADD
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/Lemoners/Attention-Is-All-You-Need
https://github.com/LeoWood/bert-horovod
https://github.com/LeonardoBerti07/Axial-LOB-High-Frequency-Trading-with-Axial-Attention
https://github.com/LiamMaclean216/Pytorch-Chatbot
https://github.com/LiamMaclean216/Pytorch-Transfomer
https://github.com/Linar23/Research_work
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/LiuChuang0059/Complex-Network
https://github.com/LiuNull/pix2pix_Liu
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/LogAnalysisTeam/loglizer
Anomaly Detection using Autoencoders in High Performance Computing Systems
https://github.com/LoopTactics/tc-cim
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
https://github.com/LorenzoAgnolucci/BERT_for_ABSA
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/LoveYang/bert_test
https://github.com/Lsdefine/attention-is-all-you-need-keras
https://github.com/Lum1104/MER-Factory
Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning
https://github.com/Lycle/ICNN2SP
https://github.com/M155K4R4/fastText
https://github.com/MCKnaus/dmlmt
https://github.com/MCLYang/RhythmGAN_pytorch
https://github.com/MINGG2333/bert_ms
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MK-tech20/CGA2M_plus
Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions
https://github.com/MKFMIKU/VIDM
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/MLCS-Yonsei/ddpg-control
https://github.com/MLH-Fellowship/Social-BERTerfly
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.