Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/johngear/eecs504
https://github.com/johnnylu305/resnet-50-101-152
https://github.com/jolibrain/caffe
https://github.com/jonasz/progressive_infogan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/jonathandrnd/Face-Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/jonathangiguere/Emotion_Image_Classifier
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
https://github.com/joonson/yousaidthat
https://github.com/joovvhan/ecapa-tdnn
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/jooyae/NVIDIA_STYLEGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jooyounghun/AI-Team-5
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/jordan-bird/synthetic-fruit-image-generator
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jtcass01/SIMON
https://github.com/jtchen0528/PCL-I2G
https://github.com/jtiger958/pytorch-computer-vision-basic
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jtiger958/pytorch-computer-vision-tutorial
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jtpils/treegan
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/juergenlandauer/Maya-Challenge
https://github.com/julik43/IIMAS-USCS
https://github.com/junebeomstics/tff
https://github.com/justinbellucci/cnn-visualizations-pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/justinjohn0306/EverybodyDanceNow-Colab
https://github.com/justinshenk/fer
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/justusschock/shape-constrained-network
https://github.com/jvoynow/ConvNet-Architectures
https://github.com/k-miran/hear
https://github.com/kabrau/FaceDetection
https://github.com/kagami/go-face
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/kaichengyan/facenet-darknet-pytorch
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/kainoj/keep-pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/kaist-dmlab/SELFIE
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kaiyangzhou/on-device-dg
https://github.com/kakaoenterprise/BroadFace
BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition
https://github.com/kalaivani-s/self-attentive-sent-emb
https://github.com/kalelpark/ral
https://github.com/kamanphoebe/motiondetection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.