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/enhuiz/transformer-pytorch
https://github.com/enod/arxiv-nlp-notes
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/enzomuschik/distilfnd
r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
https://github.com/epcpu/deep-nlp-links
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/epfml/collaborative-attention
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ercbunny/isaacgymenvs
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/eric-erki/Visualising-Facial-Expression-Changes
https://github.com/eric-yim/distribution_reconstruction
https://github.com/eric-yim/image_reconstruction
https://github.com/eric-yim/vqvae
https://github.com/ericperfect/Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder
https://github.com/ericxsun/fastText
https://github.com/eshaanagarwal/hr-net-implementation
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/eth-pbl/elmap-rl-controller
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/ethancaballero/neural-engineers-first-attempt
https://github.com/etjoa003/explainable_ai
Two Instances of Interpretable Neural Network for Universal Approximations
https://github.com/eunseo1092/Graduation_project
https://github.com/evtaktasheva/dependency_extraction
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ewdowiak/Sicilian_Translator
https://github.com/exa-labs/exa-mcp-server
https://github.com/explainingai-code/StableDiffusion-PyTorch
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/explosion/floret
https://github.com/extensive-nlp/TSAI-DeepNLP-END2.0
https://github.com/fabiankaechele/samplingfromautoencoders
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
https://github.com/fabiocorreacordeiro/Elsevier_abstracts-Classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/facebookresearch/Horizon
https://github.com/facebookresearch/TensorComprehensions
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
https://github.com/facebookresearch/anli
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/facebookresearch/msn
High Fidelity Visualization of What Your Self-Supervised Representation Knows About
https://github.com/facebookresearch/pytext
https://github.com/facebookresearch/rl/blob/main/examples/ddpg/ddpg.py
https://github.com/facebookresearch/tacto
https://github.com/facebookresearch/xformers
https://github.com/factoryIO/1-simple_neural_style_transfer
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
https://github.com/faddey-w/face-swap
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.