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/hsinyilin19/resnetvae
https://github.com/hslrock/TripletLoss_Pytorch
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/huangshenneng/padd_retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/huashen218/multiturncleanup
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
https://github.com/huawenwei4/dfl
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/hubert0527/COCO-GAN
https://github.com/hui-po-wang/hijackgan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/hukangli/https-github.com-deepinsight-insightface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/hukkelas/DSFD-Pytorch-Inference
https://github.com/hukkelas/progan-pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/human-analysis/RankGAN
https://github.com/human-analysis/secure-face-matching
https://github.com/humbleOldSage/Triplet-Loss-for-Personal-dataset
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/hustic/TinyImageSet
https://github.com/hustvl/sparseinst
https://github.com/huybik/GAN-Experiments-Pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/huybik/GANs-Pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/huyhieupham/3D-Face-Recognition
Learning from Millions of 3D Scans for Large-scale 3D Face Recognition
https://github.com/huytranvan2010/RetinaNet-Understanding
https://github.com/hweersot/darknet-custom
https://github.com/hx19940102/Image-Segmentation-based-on-FCN
https://github.com/hyang0129/foodclassapp
https://github.com/hycis/TensorGraph
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hyhchaos/FakePolisher
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction
https://github.com/hysts/pytorch_resnet
https://github.com/hysts/pytorch_resnet_preact
https://github.com/hz2538/iot_JennyGo
https://github.com/hzh8311/pyhowfar
https://github.com/hzlsaber/sida
https://github.com/i6092467/diff-bias-proxies
https://github.com/iArunava/ResNet
https://github.com/iChenning/facedetection
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/iPRoBe-lab/semi-adversarial-networks
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
https://github.com/iacopomasi/face_specific_augm
https://github.com/iamgroot42/A-LINK
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.