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/rosinality/vq-vae-2-pytorch
https://github.com/roysaptaumich/HD-bandit-codes
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits
https://github.com/rpuiggari/bert2
https://github.com/rramjee/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/rrwng/CPM
Capacity Preserving Mapping for High-dimensional Data Visualization
https://github.com/rsha0812/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/rucmlcv/L2M-GAN
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/rui1223/confined-space-rearrangement
Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces
https://github.com/rui1223/confined_space_rearrangement
Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces
https://github.com/rupakdas18/Research-Project-UMD-CS-5642
https://github.com/rupakdas18/SemEval-2017-Task-4-A-B-C-using-BERT
https://github.com/rupakdas18/SuperGlue-tasks-using-BERT
https://github.com/rwightman/efficientdet-pytorch
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/ryanxingql/image-quality-assessment-toolbox
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/ryersonvisionlab/two-stream-dyntex-synth
Two-Stream Convolutional Networks for Dynamic Texture Synthesis
https://github.com/ryojitanabe/ela_drframework
High Dimensional Bayesian Optimization Assisted by Principal Component Analysis
https://github.com/ryokamoi/cnn_for_sentence_classification
https://github.com/s-sd/task-amenability
https://github.com/s9891326/Fine-Tuning-BERT
https://github.com/saberpowers/causalLearning
Some methods for heterogeneous treatment effect estimation in high-dimensions
https://github.com/sagorbrur/bangla-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sagorbrur/codeswitch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sagy101/SoundGAN
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/sahilkhose/floodnet_vqa
Semi-Supervised Classification and Segmentation on High Resolution Aerial Images
https://github.com/saic-mdal/lama
Feature Refinement to Improve High Resolution Image Inpainting
https://github.com/sajadalipour7/persian-grapheme-to-phoneme-with-transformer
https://github.com/sajjjadayobi/ParsBigBird
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/salesforce/GeDi
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/salesforce/localization-xml-mt
A High-Quality Multilingual Dataset for Structured Documentation Translation
https://github.com/samb-t/unleashing-transformers
https://github.com/samgoldman97/ms-pred
Efficiently predicting high resolution mass spectra with graph neural networks
https://github.com/samgregoost/Spectral-GAN
https://github.com/samiatto/colide
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
https://github.com/samiranrl/ODSC_2019_RL
https://github.com/samirsahoo007/Audio-and-Speech-Processing
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.