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/arsfutura/face-recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/artemmavrin/focal-loss
https://github.com/arturjordao/arturjordao
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method
https://github.com/artxtech/darknet-rnn
https://github.com/artynet/darknet-alexeyAB
https://github.com/ashawkey/RAD-NeRF
Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition
https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras
https://github.com/ashokpant/video_processing_opencv_cpp
https://github.com/ashushekar/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ashutosh1919/FaceGenerationStyleGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/asmadotgh/unc-net
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/asprenger/keras_fc_densenet
https://github.com/astoff/dog-breed-app
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/astoycos/Mini_Project2
https://github.com/atom06/Tiny_Faces_in_Tensorflow_v2
https://github.com/audio-df-ucb/clonedvoicedetection
https://github.com/aurelien-peden/Deep-Learning-paper-implementations
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/autonomobil/SCDND-P2_Traffic-Sign-Classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/autonomousvision/frequency_bias
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/av-savchenko/emotiefflib
Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices
https://github.com/avishai111/One-Class-Learning-with-Adaptive-Centroid-Shift-for-Audio-Deepfake-Detection
One-Class Learning with Adaptive Centroid Shift for Audio Deepfake Detection
https://github.com/avocardio/resnet_vs_convnext
https://github.com/avuku06/G
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
https://github.com/axium/Blind-Image-Deconvolution-using-Deep-Generative-Priors
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/axmoyal/Steganalysis-with-CNN
https://github.com/ayushdabra/drone-images-semantic-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/lunar-landscape-images-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/retinal-oct-images-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushgupta9198/gan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushgupta9198/stylegan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushpalak/darknet_cokebottles
https://github.com/ayushpalak/darknet_multi_cokebottles
https://github.com/azshue/TPT
https://github.com/b5071/pulse-rev
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/b8goal/PIRL-Residual-network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.