https://github.com/hongshuochen/DefakeHop
공식 코드DefakeHop: A Light-Weight High-Performance Deepfake Detector
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/hotfinda/Compensation-sampling
공식 코드Compensation Sampling for Improved Convergence in Diffusion Models
https://github.com/huage001/dynast
공식 코드DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
https://github.com/huangjiadidi/DeepFakeMnist
공식 코드https://github.com/huangyangyu/ADNet
공식 코드ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment
https://github.com/huangyangyu/NoiseFace
공식 코드https://github.com/huangyangyu/SeqFace
공식 코드SeqFace: Make full use of sequence information for face recognition
https://github.com/huashen218/llm-deepfake-human-study
공식 코드Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
https://github.com/huitailangyz/AFGAN
공식 코드Attributes Aware Face Generation with Generative Adversarial Networks
https://github.com/humanlab/hart
공식 코드https://github.com/humansensinglab/PATMAT
공식 코드PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting
https://github.com/hurricanelx/lrfr
공식 코드https://github.com/hwang1996/3D-Cartoon-Face-Generation
공식 코드3D Cartoon Face Generation with Controllable Expressions from a Single GAN Image
https://github.com/hxngiee/DiffFace
공식 코드DiffFace: Diffusion-based Face Swapping with Facial Guidance
https://github.com/i13abe/Adaptive-discriminant-and-center
공식 코드https://github.com/iamsangmeng/interface
공식 코드InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition
https://github.com/iamwangyabin/poundnet
공식 코드https://github.com/ibug-group/face_reid
공식 코드A real-time and unsupervised face Re-Identification system for Human-Robot Interaction
https://github.com/ibug-group/fpage
공식 코드FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild
https://github.com/idstcv/mogface
공식 코드https://github.com/idt-iti/lightweight-face-detector-pruning
공식 코드Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterion
https://github.com/idt-iti/xai-deepfakes
공식 코드Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection
https://github.com/igor-morawski/tmm-sem
공식 코드Leveraging Content and Context Cues for Low-Light Image Enhancement
https://github.com/igsb/gestaltmatcher-arc
공식 코드Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles
https://github.com/ihp-lab/fg-net
공식 코드FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features
https://github.com/iman2693/gdsm
공식 코드Brain Age Estimation with a Greedy Dual-Stream Model for Limited Datasets
https://github.com/imatge-upc/wav2pix
공식 코드Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
https://github.com/iperov/DeepFaceLab
공식 코드DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/iremeyiokur/multipie_extended_ear_dataset
공식 코드Multimodal Age and Gender Classification Using Ear and Profile Face Images
https://github.com/irisdominguez/dataset-bias-metrics
공식 코드https://github.com/irvingmeng/securevector
공식 코드Towards Privacy-Preserving, Real-Time and Lossless Feature Matching
https://github.com/isi-vista/face-completion
공식 코드https://github.com/isspek/cross-lingual-cyberbullying
공식 코드Unified and Multilingual Author Profiling for Detecting Haters
https://github.com/ivclab/CPG
공식 코드Compacting, Picking and Growing for Unforgetting Continual Learning
https://github.com/ivclab/Online-Face-Recognition-and-Authentication
공식 코드Data-specific Adaptive Threshold for Face Recognition and Authentication
https://github.com/ivonacolakovic/synthgap-mitigation-using-kd-in-ffr
공식 코드How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.