https://github.com/faysal-md/unmasking-deepfake-faces-from-videos-an-explainable-cost-sensitive-deep-learning-approach-ieee2023
공식 코드Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive Deep Learning Approach
Papers with Code 아카이브
3D Face Modelling
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fbiying87/demographic-bias-visualization
공식 코드Towards Explaining Demographic Bias through the Eyes of Face Recognition Models
https://github.com/fbiying87/explainable_fiqa_with_amva
공식 코드https://github.com/fdbtrs/AdaDistill
공식 코드AdaDistill: Adaptive Knowledge Distillation for Deep Face Recognition
https://github.com/fdbtrs/ElasticFace
공식 코드https://github.com/fdbtrs/IDiff-Face
공식 코드IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models
https://github.com/fdbtrs/QuantFace
공식 코드QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization
https://github.com/fdbtrs/Self-restrained-Triplet-Loss
공식 코드Self-restrained Triplet Loss for Accurate Masked Face Recognition
https://github.com/fdbtrs/Unsupervised-Face-Recognition-using-Unlabeled-Synthetic-Data
공식 코드Unsupervised Face Recognition using Unlabeled Synthetic Data
https://github.com/fdbtrs/cr-fiqa
공식 코드CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability
https://github.com/fdbtrs/exfacegan
공식 코드https://github.com/fdbtrs/masked-face-recognition-kd
공식 코드Mask-invariant Face Recognition through Template-level Knowledge Distillation
https://github.com/fdbtrs/mixfacenets
공식 코드https://github.com/fdbtrs/pocketnet
공식 코드https://github.com/fdbtrs/sface-privacy-friendly-and-accurate-face-recognition-using-synthetic-data
공식 코드SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data
https://github.com/fdbtrs/synthetic-face-recognition
공식 코드Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition
https://github.com/fdnerf/fdnerf
공식 코드FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing
https://github.com/fedeloper/binary_deepfake_detection
공식 코드Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks
https://github.com/fedenoce/s2l-s2d
공식 코드Learning Landmarks Motion from Speech for Speaker-Agnostic 3D Talking Heads Generation
https://github.com/fei-hdu/ca-gan
공식 코드Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs
https://github.com/fei-hdu/sRender
공식 코드Bridging Unpaired Facial Photos And Sketches By Line-drawings
https://github.com/felixrosberg/facedancer
공식 코드FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping
https://github.com/fengju514/Expression-Net
공식 코드https://github.com/fengju514/Face-Pose-Net
공식 코드https://github.com/fengyuentau/pytorch-flops
공식 코드https://github.com/ff93/parameter-based-value-functions
공식 코드https://github.com/filby89/spectre
공식 코드Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos
https://github.com/fiorenza2/OffCon3
공식 코드https://github.com/flairox/behaviour-distillation
공식 코드https://github.com/flakeua/bachelorsthesis
공식 코드Appearance-based gaze estimation enhanced with synthetic images using deep neural networks
https://github.com/flyingby/awesome-deepfake-generation-and-detection
공식 코드https://github.com/fodark/anonygan
공식 코드Graph-based Generative Face Anonymisation with Pose Preservation
https://github.com/forblindrev/aibias
공식 코드Fair and accurate age prediction using distribution aware data curation and augmentation
https://github.com/forever208/fmae-iat
공식 코드Representation Learning and Identity Adversarial Training for Facial Behavior Understanding
https://github.com/forget2save/campro
공식 코드https://github.com/frankzheng2022/prise
공식 코드PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.