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/samwisegamjeee/pytorch-transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/samyakjain0112/Video-denoising_tensorflow-core-api-2.0
https://github.com/saoudh/Reinforcement-Learning
https://github.com/sarahromanes/multiDA
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data
https://github.com/saravanakumarjsk/BERT-collection
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sarthaxxxxx/Attention-is-all-you-need
https://github.com/sarus-tech/tf2-published-models
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/satyajitghana/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/satyajitghana/TSAI-DeepNLP-END2.0
https://github.com/saurabhkulkarni77/BERT_multilabel
https://github.com/saurabhnlp/bert
https://github.com/sauram/Machine-Translation-using-Attention-Model
https://github.com/sayakpaul/Adaptive-Gradient-Clipping
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/sayantanauddy/hierarchical_bipedal_controller
https://github.com/sbelharbi/feature-vs-text-compound-emotion
Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild
https://github.com/sberbank-ai/ner-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/schatty/D4PG-pytorch
https://github.com/scikit-learn-contrib/categorical-encoding
Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems
https://github.com/scotthlee/injury_autocoding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/scrayish/ML_NLP
https://github.com/scut-bds/example_repo_from_scutbds
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sdll/hrnet-pose-estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/seawander/aastex_pwned
Using Data Imputation for Signal Separation in High Contrast Imaging
https://github.com/seenu-g/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/seepala98/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/semal/bert
https://github.com/semicontinuity/nlp
https://github.com/sense-x/tokenmix
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/senticnet/sc-lstm
https://github.com/seominseok0429/inception-I3D-NON-LOCAL
https://github.com/sergkuzn148/lol3
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/sergkuzn148/stg
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/seung-lab/kimimaro
Automatic Neuron Type Identification by Neurite Localization in the Drosophila Medulla
https://github.com/seunghwan1228/Transfomer-MachineTranslation
https://github.com/seungjaeryanlee/osim-rl-helper
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.