https://github.com/jason90330/EdgeFinal
RetinaFace: Single-stage Dense Face Localisation in the Wild
Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 26
- 평가표
- 0
- 보존된 평가 기록
- 0
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/jattardo1/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/javier-hernandezo/faceqgen
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
https://github.com/javier-hernandezo/faceqnet
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
https://github.com/jaydeepthik/kaggle-facial-expression-recognition
Convolutional Neural Networks for Facial Expression Recognition
https://github.com/jchaykow/AGN-pytorch
A General Framework for Adversarial Examples with Objectives
https://github.com/jday96314/Kuzushiji
https://github.com/jehovahxu/ca-gan
Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs
https://github.com/jeromerony/Progressive_Growing_of_GANs-PyTorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/jesperhauch/polynomial_deep_learning
https://github.com/jesse1029/SiGAN
SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination
https://github.com/jessica3724/Keras_VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jessiemjones/TF-2.0-Neural-Style-Transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jfc4050/detect-to-track
https://github.com/jfthuong/photo-organizer
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/jhchang/DFDC
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/jiajunhua/facebookresearch-Detectron
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jiajunhua/tkarras-progressive_growing_of_gans
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/jiangxingxun/Seeking_Salient_Facial_Region
Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition
https://github.com/jiantenggei/Convolutional-Neural-Network-Hyperparameters-Optimization-for-Facial-Emotion-Recognition
https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop
DenseBox: Unifying Landmark Localization with End to End Object Detection
https://github.com/jimmy0087/faceai-master
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/jingyuanz/keras-self-attention-layer
https://github.com/jis478/AniGAN
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
https://github.com/jiweibo/imagenet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jixinya/EVP
https://github.com/jjmachan/DeepHash
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/jkvt2/Latent-Pose-Descriptors
https://github.com/jm199504/VGG-NET
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jmysu/ESP32CAM-FaceDetect-Line-Notify
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/jndeng/DACSDC-DeepZ
https://github.com/jnhwkim/cbp
https://github.com/jnulzl/Pytorch_Retinaface_To_Caffe
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/joaocneves/gan_fingerprint_removal
GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
https://github.com/joehoeller/Computer-Vision-Facial-Key-Point-Detection
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/joeynavarro/facial_expression_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.