https://github.com/cobermai/rfstudies
공식 코드Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities
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/coleygroup/molpal
공식 코드Accelerating high-throughput virtual screening through molecular pool-based active learning
https://github.com/conor-horgan/DeepeR
공식 코드High-throughput molecular imaging via deep learning enabled Raman spectroscopy
https://github.com/core-robotics-lab/icct
공식 코드Learning Interpretable, High-Performing Policies for Autonomous Driving
https://github.com/cpempire/pWGD
공식 코드Projected Wasserstein gradient descent for high-dimensional Bayesian inference
https://github.com/cqcl/lambeq
공식 코드lambeq: An Efficient High-Level Python Library for Quantum NLP
https://github.com/creggian/spark-ifs
공식 코드Feature selection in high-dimensional dataset using MapReduce
https://github.com/crgagne/twosteps_neurips2021
공식 코드https://github.com/croitorualin/lerac
공식 코드https://github.com/cryptonymous9/Augmented-ConvLSTM
공식 코드Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections
https://github.com/cscvlab/3D-Objects-Compression
공식 코드https://github.com/csebuetnlp/banglaparaphrase
공식 코드https://github.com/csho33/bacteria-ID
공식 코드Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
https://github.com/csj199813/a2net_ecpe
공식 코드Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction
https://github.com/csliuwei/MI_plot
공식 코드Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis
https://github.com/curto2/c
공식 코드High-Resolution Deep Convolutional Generative Adversarial Networks
https://github.com/cv-stuttgart/MS_RAFT_plus
공식 코드High Resolution Multi-Scale RAFT (Robust Vision Challenge 2022)
https://github.com/cvdfoundation/google-landmark
공식 코드Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
https://github.com/cvlab-epfl/mvaug
공식 코드Two-level Data Augmentation for Calibrated Multi-view Detection
https://github.com/cvlab-epfl/zigzag
공식 코드ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference
https://github.com/cw-wan/EmoDynamiX-v2
공식 코드https://github.com/cwang621/blsp-emo
공식 코드https://github.com/cwchenwang/NeRF-SR
공식 코드NeRF-SR: High-Quality Neural Radiance Fields using Supersampling
https://github.com/cyh4/FCTSFN
공식 코드A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation
https://github.com/cyhuang-tw/speechcaps
공식 코드https://github.com/cyrilzakka/stylegan2-tpu
공식 코드MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education
https://github.com/czk32611/GEDDnet
공식 코드Towards High Performance Low Complexity Calibration in Appearance Based Gaze Estimation
https://github.com/czl0914/psy_hotline_analysis
공식 코드https://github.com/czyssrs/Logic2Text
공식 코드Logic2Text: High-Fidelity Natural Language Generation from Logical Forms
https://github.com/d-li14/HBONet
공식 코드https://github.com/daiweiworking/RobustDeepLearningUsingPerturbations
공식 코드Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
https://github.com/dalaoplan/Happy-rPPG-Toolkit
공식 코드https://github.com/dalaoplan/happp-rppg-toolkit
공식 코드https://github.com/danaesavi/poi-type-prediction
공식 코드https://github.com/danielathome19/Sung-EmotioNN-Detector
공식 코드Emotion Recognition of the Singing Voice: Toward a Real-Time Analysis Tool for Singers
https://github.com/daooshee/HLA-Face-Code
공식 코드HLA-Face: Joint High-Low Adaptation for Low Light Face Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.