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/jihaoxuanye/MetaPRD
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/jim-schwoebel/allie
https://github.com/jincan333/lot
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
https://github.com/jingyunx/Deformation-Flow-Based-Two-stream-Network
https://github.com/jinzhenfan/BERT
https://github.com/jiwei0921/Saliency-Evaluation-Toolbox
https://github.com/jiyuan/ainote
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jjfeng/easier_net_R
Ensembled sparse-input hierarchical networks for high-dimensional datasets
https://github.com/jl2922/fgpl
https://github.com/jleinonen/keras-fid
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/jlmelville/uwot
https://github.com/jmnolte/hccnet
https://github.com/joannahong/av-relscore
Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets
https://github.com/joanrod/figure-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/joanrod/ocr-vqgan
https://github.com/joebluems/Soul_Of_AI
https://github.com/joernio/joernti-codetidal5
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
https://github.com/joeykay9/offenseval
Predicting the Type and Target of Offensive Posts in Social Media
https://github.com/jogo175/treediffusion
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents
https://github.com/joh-fischer/PlantLDM
https://github.com/john-mnz/ComfyUI-Inspyrenet-Rembg
Revisiting Image Pyramid Structure for High Resolution Salient Object Detection
https://github.com/johnr0/TaleBrush-backend
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jojo13572001/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/joongbo/tta
https://github.com/joytianya/google_bert
https://github.com/jpzwolak/QFlow-lite
Ray-based classification framework for high-dimensional data
https://github.com/jsantoso2/yelp-clone-ml-project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/juangamella/stars
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models
https://github.com/julesberman/colora
Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution Equations
https://github.com/juliasymbolics/metatheory.jl
https://github.com/jungomi/swiss-language-model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/junhahyung/bert_finetune
https://github.com/junhahyung/bert_transfer
https://github.com/junzhin/dgm-vlc
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/jwmueller/BanditDynamicPricing
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.