Papers with Code 아카이브
Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 10
- 보존된 평가 기록
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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/TheNeuromancer/SentEmb
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https://github.com/TheTensorDude/transformer-tf
https://github.com/TidalPaladin/neural-summarizer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/TideDancer/interspeech21_emotion
https://github.com/TieDanCuihua/transformer-Attention-is-All-You-Need
https://github.com/TinkerBoard2-Android/external-XNNPACK
https://github.com/TobiasLee/Text-Classification
https://github.com/TonyX19/bert_hyperpartisan
https://github.com/Trevillie/MagNet
https://github.com/TropComplique/FaceBoxes-tensorflow
https://github.com/Trustworthy-AI-Group/TransferAttack
Diversifying the High-level Features for better Adversarial Transferability
https://github.com/TsingZ0/PFL-Non-IID
https://github.com/UBC-Computer-Vision-Group/DwNet
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/UC-MACSS/persp-analysis_A18
The Geometry of Culture: Analyzing Meaning through Word Embeddings
https://github.com/UNHSAILLab/Sentimental-LIAR-Extended-Corpus-and-Deep-Learning-Models-for-Fake-Claim-Classification
Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification
https://github.com/UdbhavPrasad072300/GANs-Implementations
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/UdbhavPrasad072300/Transformer-Implementation
https://github.com/UdbhavPrasad072300/Transformer-Implementation-and-Language-Translation
https://github.com/UdbhavPrasad072300/Transformer-Implementations
https://github.com/UesugiErii/tf2-PPO-atari
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/Unbabel/COMET
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/UnknownGenie/altered-BERT-KPE
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/UnofficialJuliaMirror/Circuitscape.jl-2b7a1792-8151-5239-925d-e2b8fdfa3201
Circuitscape in Julia: High Performance Connectivity Modelling to Support Conservation Decisions
https://github.com/Valentin4869/BinCNN
https://github.com/Valentin4869/MachineLearning
https://github.com/VamsiKrishna1211/gender_age-detection
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
https://github.com/VasaKiDD/TD3-deep-rl-research
https://github.com/VedantYadav/Transformer
https://github.com/Vill-Lab/2022-TIP-HCGA
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/VincieD/TrafficSign_GAN_Benchmark
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/VladAndronik/face_swap
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/Vladimetr/ASR-Knowledge-Transferring
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Walter-B/bert-20-classes
https://github.com/Wang-ZH-Stat/IVFS
IVFS: Simple and Efficient Feature Selection for High Dimensional Topology Preservation
https://github.com/WangWenhao0716/DomainMix
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.