Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/cem8301/EmotionDetector
https://github.com/cfl0122/face
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/chamikara1986/BA-BAM
https://github.com/changsukim-ku/order-learning
https://github.com/chayanchatterjee/cbc-skynet
https://github.com/chayanchatterjee/gw-skylocator
https://github.com/chelvanai/Oneshot-Face-Identify
https://github.com/chengcv/J3S
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
https://github.com/chenggongliang/arcface
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/chenqiang19/vgg_real_app_age_regression
On the effect of age perception biases for real age regression
https://github.com/chenyeheng/SmartCar
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/chenyeheng/SmartCar-FaceRec
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/chenyeheng/SmartCar-FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/chinakook/hr101_mxnet
https://github.com/chinhau-lim/fer2013
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/chinhau-lim/fer_2013
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/chldydgh4687/2020-1.VideoCaptioning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/chosj95/PFPNet.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/choyingw/GD-HASLR
Occluded Face Recognition Using Low-rank Regression with Generalized Gradient Direction
https://github.com/chrhenning/hypercl
https://github.com/chrisqqq123/FA-Dist-EfficientNet
https://github.com/christy1206/STSTNet
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
https://github.com/chroniccrash/facestuff
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/cindyloo/rockstar-ai
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/civilman628/pgan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/cj-mclaughlin/segmentation_research
https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ck37/coral-ordinal
Rank consistent ordinal regression for neural networks with application to age estimation
https://github.com/ckapoor7/DeXpression
DeXpression: Deep Convolutional Neural Network for Expression Recognition
https://github.com/clairehester/face-mask-detector
Deep Learning Framework to Detect Face Masks from Video Footage
https://github.com/claushofmann/GAN-game-art
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/clcarwin/sphereface
https://github.com/clcarwin/sphereface_pytorch
https://github.com/clinicalml/parametric-robustness-evaluation
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
https://github.com/cloudnine148/PHC_2nd_SPC
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.