https://github.com/git-disl/EnsembleBench
공식 코드Promoting High Diversity Ensemble Learning with EnsembleBench
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/gitstevendeng/two-halves-statistically-different
공식 코드https://github.com/gizemsogancioglu/elderly-emotion-SC
공식 코드https://github.com/glam-imperial/AudioXgen
공식 코드Audio Explanation Synthesis with Generative Foundation Models
https://github.com/glam-imperial/nsl-ser
공식 코드Knowledge Transfer For On-Device Speech Emotion Recognition with Neural Structured Learning
https://github.com/glancia93/ICUAI-dynamic-prediction
공식 코드Two-step interpretable modeling of Intensive Care Acquired Infections
https://github.com/glmanhtu/multitask-abaw-2021
공식 코드https://github.com/godxuxilie/robust-tst
공식 코드Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
https://github.com/google-research/bert
공식 코드BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/google-research/hit-gan
공식 코드https://github.com/google-research/lag
공식 코드Creating High Resolution Images with a Latent Adversarial Generator
https://github.com/google-research/lasertagger
공식 코드https://github.com/google-research/robopianist
공식 코드RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning
https://github.com/google-research/sloe-logistic
공식 코드SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression
https://github.com/google/XNNPACK
공식 코드https://github.com/google/compare_gan
공식 코드https://github.com/google/mediapipe
공식 코드Attention Mesh: High-fidelity Face Mesh Prediction in Real-time
https://github.com/google/zoom-to-inpaint
공식 코드Zoom-to-Inpaint: Image Inpainting with High-Frequency Details
https://github.com/gourabkumarpatro/FairRec_www_2020
공식 코드FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
https://github.com/gradhep/neos
공식 코드neos: End-to-End-Optimised Summary Statistics for High Energy Physics
https://github.com/graphpku/2wl_link_pred
공식 코드Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction
https://github.com/graytowne/rank_distill
공식 코드Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
https://github.com/gregversteeg/CorEx
공식 코드Discovering Structure in High-Dimensional Data Through Correlation Explanation
https://github.com/griegler/octnet
공식 코드OctNet: Learning Deep 3D Representations at High Resolutions
https://github.com/gsoumyajit/actor-critic-critic-actor
공식 코드https://github.com/guanghaoyin/RTCAN-1D
공식 코드https://github.com/guangyizhangbci/eeg_riemannian
공식 코드Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space
https://github.com/guangyizhangbci/parse
공식 코드PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition
https://github.com/guangyizhangbci/pll-emotion-eeg
공식 코드https://github.com/guanjiyang/sac_jc
공식 코드https://github.com/gudgud96/noisy-student-emotion-training
공식 코드https://github.com/guosheng/refinenet
공식 코드RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/guoyongcs/AEGAN
공식 코드Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis
https://github.com/guoyuntu/BEAC_network
공식 코드A Multi-task Neural Approach for Emotion Attribution, Classification and Summarization
https://github.com/gurkirt/AMTNet
공식 코드https://github.com/guyhorowitz/csc
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.