https://github.com/MNaplesDevelopment/DCGAN-PyTorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
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/MNaplesDevelopment/Deep-Convolutional-Generative-Adversarial-Networks
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/MNaplesDevelopment/MNIST-DCGAN-PyTorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/mixnet
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnet
https://github.com/MS-Mind/MS-Code-06/tree/main/PGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/MalayAgarwal-Lee/MesoNet-DeepFakeDetection
https://github.com/MaliParag/ScanSSD
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Maninder10/face
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/ManyiLee/Open-world-Deepfake-Detection-Network
ODDN: Addressing Unpaired Data Challenges in Open-World Deepfake Detection on Online Social Networks
https://github.com/MariefW/6.-trainingYOLO
https://github.com/MarkHershey/arxiv-dl
https://github.com/Martion-z/Paddle-PULSE
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/Martion-z/pytorch-Pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/Masao-Taketani/FOTS_OCR
https://github.com/Maskify/darknet
https://github.com/MatGod7/Retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/MathiasStensrud/autoBleep
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Maveric4/pr19aaw01
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MaxLikesMath/DeepLearningImplementations
DeXpression: Deep Convolutional Neural Network for Expression Recognition
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Network_In_Network
https://github.com/MaximilianHollis/Maskify
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
https://github.com/MaxwellHeller/coffee_fraud_detection
https://github.com/Mayurji/Image-Classification-PyTorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MegEngine/Models/tree/master/official/vision/classification/resnet
https://github.com/MegEngine/Models/tree/master/official/vision/detection
https://github.com/Meghraj-Webllisto/stylegan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MiX-S/resnet_cifar10
https://github.com/MichaelWangfc/distributed-tensorflow-resnet
https://github.com/Mimigatto/face-mask-detection
MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19
https://github.com/Mind23-2/MindCode-101/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/Mind23-2/MindCode-101/tree/main/LightCNN
https://github.com/Mind23-2/MindCode-101/tree/main/MTCNN
Real-world adversarial attack on MTCNN face detection system
https://github.com/Mind23-2/MindCode-101/tree/main/MaskedFaceRecognition
https://github.com/Mind23-2/MindCode-101/tree/main/lresnet100e_ir
https://github.com/Mind23-2/MindCode-112
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.