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/songquanpeng/BlendGAN
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
https://github.com/soubhiksanyal/FLAME_PyTorch
https://github.com/soubhiksanyal/RingNet
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
https://github.com/soubhiksanyal/now_evaluation
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
https://github.com/soumik12345/Nearest-Celebrity-Face
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/soumik12345/Zero-DCE
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/soumik12345/point-cloud-segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/soumith/convnet-benchmarks
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/souravverma94/pre_trained_imgclassifier_dogBreeds
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sovit-123/Deep-Learning-Image-Captioning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sppsps/VGG-16-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/srihari-humbarwadi/retinanet-tensorflow2.x
https://github.com/srihari-humbarwadi/tensorflow_retinanet
https://github.com/srilakshmiv14/FaceNet_Pytorch
https://github.com/srvcodes/clap4clip
https://github.com/starhiking/ATF
https://github.com/stdereka/knowledge-distillation
An Embarrassingly Simple Approach for Knowledge Distillation
https://github.com/stefkim/stylegan-batik
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/stergioc/smooth-transformer
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
https://github.com/stevehansen43/progressive_GAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/stevenzhou2017/darknet_AlexeyAB
https://github.com/stresearch/xfr
https://github.com/strivebo/image_segmentation_dl
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/sudhanshu2k/Signature-Verification
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sudharavali/objectDetectionYOLO
https://github.com/suhas1999/Flip-kart-grid-challenge
https://github.com/suhuijia/pggan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/sukibean163/RetinaFace_Paddle
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/sulasen/race-events-recognition-1
https://github.com/sumathi16/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sumitkutty/Anti-Spoof-Face-Recognition
https://github.com/summerice9/AniGAN
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
https://github.com/sungwool/virtual_softmax
https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet
https://github.com/sunil-rival/insightface
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.