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/htzheng/CM-GAN-Inpainting
High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling
https://github.com/huanghonggit/Mask-Language-Model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hubiodatalab/druggen
https://github.com/hufscapstone/Fast_text
https://github.com/huggingface/transformers
https://github.com/hugozanini/realtime-semantic-segmentation
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/huihui0902/robust_mean_estimation
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering
https://github.com/huisun317/snn
Solving high-dimensional partial differential equations using deep learning
https://github.com/hukkelas/pytorch-frechet-inception-distance
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/humanmllm/r1-omni
R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning
https://github.com/hwang9u/hum2Guitar
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/hyn2028/llm-cxr
https://github.com/hyunwookl/testam
https://github.com/hyunwoongko/transformer
https://github.com/hzli-ucas/StarGraph
StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph
https://github.com/iDataist/Continuous-Control-with-Deep-Deterministic-Policy-Gradient
https://github.com/iDataist/Tennis-With-Multi-Agent-Reinforcement
https://github.com/iamalexkorotin/KernelNeuralOptimalTransport
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/iamalexkorotin/Wasserstein2Benchmark
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/iamalexkorotin/wassersteiniterativenetworks
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/iamjeff7/j-va-aagcn
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/iamshnoo/soc_bias
Socially Aware Bias Measurements for Hindi Language Representations
https://github.com/ibaiGorordo/PyTorch-High-Res-Stereo-Depth-Estimation
https://github.com/iceli1007/HFC-and-HFF
Exploring The Effect of High-frequency Components in GANs Training
https://github.com/icewing1996/bert_dep
https://github.com/ictnlp/DiverseNMT
https://github.com/ictnlp/NA-MNMT
https://github.com/ictnlp/PTE-NMT
https://github.com/ictnlp/TLAT-NMT
https://github.com/idiotprofessorchen/bert.github.io
https://github.com/iejMac/ScriptWriter
https://github.com/ikergarcia1996/Iker-Garcia-Ferrero
T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks
https://github.com/ikergarcia1996/Simultaneous-Machine-Translation
https://github.com/ilhamfp/indonesian-text-classification-multilingual
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ilonatommy/DLR_FacialEmotionRecognition
Image Classification by Reinforcement Learning with Two-State Q-Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.