https://github.com/jxzhangjhu/MatDesINNe
Inverse design of two-dimensional materials with invertible neural networks
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/k-miran/hear
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/kacperChwialkowski/analyticMeanEmbeddings
Fast Two-Sample Testing with Analytic Representations of Probability Measures
https://github.com/kaituoxu/Speech-Transformer
https://github.com/kaituoxu/SpeechTransformer
https://github.com/kamalkraj/BERT-SQuAD
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kamalkraj/BERT_NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kanhereashwin/cs236-project
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/karenacorn99/explore-bert
https://github.com/karpathy/makemore
https://github.com/karpathy/minGPT
https://github.com/karta282950/bert-multiclass
https://github.com/karynaur/High-Fidelity-Pluralistic-ICT
High-Fidelity Pluralistic Image Completion with Transformers
https://github.com/karynaur/ICT
High-Fidelity Pluralistic Image Completion with Transformers
https://github.com/katiana22/GDM-PCE-surrogates
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models
https://github.com/kaushaltrivedi/fast-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kdha0727/cancer-instance-segmentation-from-tissue
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/kdhingra307/temp
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/keiyamashita111/aaproject
https://github.com/kelly2016/multi-label-bert
https://github.com/ken724049/action-recognition
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/kenanidris/deoldify
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/keonlee9420/Parallel-Tacotron2
https://github.com/keonlee9420/PortaSpeech
PortaSpeech: Portable and High-Quality Generative Text-to-Speech
https://github.com/keras-team/keras-cv
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/kh-kim/simple-nmt
https://github.com/kh-kim/simple-ntc
https://github.com/khalidsaifullaah/bertify
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/khalil-research/neur2bilo
https://github.com/khalil-research/neur2ro
https://github.com/khwajawisal/Attention-augmented-Convolutional-Neural-Networks
https://github.com/kidanger/high-noise-deblurring
https://github.com/kidist-amde/biggan-pytorch
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/kiko441500/google_bert
https://github.com/kingcheng2000/bert
https://github.com/kingcong/HRNetW48_seg
High-Resolution Representations for Labeling Pixels and Regions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.