Papers with Code 아카이브
Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/MingyuLi19910814/Convolutional-Neural-Networks-for-Sentence-Classification-tf2
https://github.com/MirunaPislar/multi-head-attention-labeller
https://github.com/Misoknisky/Bert-MultiGpu
https://github.com/Mleader2/text_scalpel
https://github.com/MohamedAbdelsalam9/TT-Transformer
https://github.com/Mohanned-Elkholy/BigGan-model-inversion-with-image-transformations
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/Mohanned-Elkholy/ResNet-GAN
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/MoonInTheRiver/DiffSinger
PortaSpeech: Portable and High-Quality Generative Text-to-Speech
https://github.com/MoritzKappel/HF-NHMT
High-Fidelity Neural Human Motion Transfer from Monocular Video
https://github.com/MorningBooks/A_oveview_of_awesome_causality_dataset
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts
https://github.com/MorningBooks/Causality
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts
https://github.com/MrSyee/pg-is-all-you-need
https://github.com/NERSC/sc21-dl-tutorial
Fast, high-fidelity Lyman $α$ forests with convolutional neural networks
https://github.com/NLPbox/feng-hirst-service
Two-pass Discourse Segmentation with Pairing and Global Features
https://github.com/NOHYC/autonomous_driving_car_project
https://github.com/NSR9/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/NU-LL/lighttrack-
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification
Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification
https://github.com/NUSTM/FacialMMT
https://github.com/NVIDIA-Omniverse/IsaacGymEnvs
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/NVIDIA/MinkowskiEngine
High-dimensional Convolutional Networks for Geometric Pattern Recognition
https://github.com/NVIDIA/sentiment-discovery
https://github.com/NVIDIA/unsupervised-video-interpolation
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
https://github.com/NVlabs/PAMTRI
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/NVlabs/eg3d
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/NVlabs/industreallib
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/NVlabs/selfsupervised-denoising
https://github.com/NatthanonNon/text-classification-benchmarks
https://github.com/Negus25/MindSpore-CNN_sentence
https://github.com/NehaTamore/TextSimilarity
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Neoanarika/torchexplainer
https://github.com/NervanaSystems/coach
https://github.com/NiallEHunt/MonocularDepth-Using-LightFields
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/NiallJeffrey/DeepMass
Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks
https://github.com/Nikolai10/MRIC
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.