https://github.com/labmlai/annotated_deep_learning_paper_implementations
High-Dimensional Continuous Control Using Generalized Advantage Estimation
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/laowang666888/HRNET
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/layel2/MagNet-pytorch
https://github.com/layumi/Person_reID_baseline_pytorch
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/lcs2-iiitd/emotion-flip-reasoning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/leeBooMla/ICSR
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/leeboomla/calr
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/leehomyc/Faster-High-Res-Neural-Inpainting
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
https://github.com/leehyunjun-seoultech/seoultech
https://github.com/leejieun51/transformer
https://github.com/leeyegy/SimDR
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/leeyegy/simcc
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/leftthomas/restormer
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/lehinevych/SAAN
https://github.com/lehoanganh298/BERT-Question-Answering
https://github.com/leminhbinh0209/add
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/lemon-liley/isabella
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/lemonhu/ner-bert-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lenacabrera/gb_mnmt
https://github.com/lennonzurich/lalala
https://github.com/leognha/PyTorch-FID-score
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/gpvit
GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/hornet
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/nfnets
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/leshanbog/lasertagger
https://github.com/lewisluk/BoundaryAwareNetwork
Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation
https://github.com/lferry007/LargeVis
https://github.com/liampetti/DDPG
https://github.com/lidongliang666/cv_deep_learning
https://github.com/lif31up/attention-is-all-you-need
https://github.com/lif31up/extended-BERT-for-low-rank-adaption
https://github.com/lightning-uq-box/lightning-uq-box
ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference
https://github.com/lilijiangg/autodiffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/lily-x/combinatorial-rmab
https://github.com/liminn/icnet-pytorch
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/linguishi/chinese_sentiment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.