https://github.com/alonzolfi/adversarialmask
공식 코드Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model
Papers with Code 아카이브
Facial Recognition and Modelling
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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/altndrr/clup
공식 코드Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition
https://github.com/amazon-research/gendered-profession-translations
공식 코드Faithful Target Attribute Prediction in Neural Machine Translation
https://github.com/amilkh/cs230-fer
공식 코드https://github.com/aminesi/robustness-on-device
공식 코드Robustness Analysis of Deep Learning Frameworks on Mobile Platforms
https://github.com/amoonfana/fastface
공식 코드FastFace: Fast-converging Scheduler for Large-scale Face Recognition Training with One GPU
https://github.com/andrekassis/DiffBreak
공식 코드https://github.com/andrekassis/ai-watermark
공식 코드UnMarker: A Universal Attack on Defensive Image Watermarking
https://github.com/andresprados/spiga
공식 코드Shape Preserving Facial Landmarks with Graph Attention Networks
https://github.com/angelomenezes/Pytorch_Face_SR
공식 코드https://github.com/anger-man/unsupervised-depth-estimation
공식 코드Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction
https://github.com/anguyen8/deepface-emd
공식 코드https://github.com/anguyen8/face-vit
공식 코드Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers
https://github.com/anjith2006/bob.paper.cross_modal_focal_loss_cvpr2021
공식 코드https://github.com/anjith2006/bob.paper.deep_pix_bis_pad.icb2019
공식 코드Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/anjith2006/bob.paper.ijcb2021_vision_transformer_pad
공식 코드On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
https://github.com/anmol2059/svdd2024
공식 코드https://github.com/anonrep/iba-for-video-sequences
공식 코드Extending Information Bottleneck Attribution to Video Sequences
https://github.com/anonymous-author-sub/seeable
공식 코드SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes
https://github.com/anonymoususera/ava
공식 코드AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection
https://github.com/antonibigata/Laughing-Matters
공식 코드Laughing Matters: Introducing Laughing-Face Generation using Diffusion Models
https://github.com/anubhav1997/youneednodataset
공식 코드A Dataless FaceSwap Detection Approach Using Synthetic Images
https://github.com/anuragranj/coma
공식 코드https://github.com/aosokin/cnn_head_detection
공식 코드https://github.com/apchenstu/Facial_Details_Synthesis
공식 코드https://github.com/aqeelanwar/MaskTheFace
공식 코드https://github.com/arassadin/emotiw2017
공식 코드Group-level Emotion Recognition using Transfer Learning from Face Identification
https://github.com/aravinda27/extswap
공식 코드ExtSwap: Leveraging Extended Latent Mapper for Generating High Quality Face Swapping
https://github.com/architect-road/icongan
공식 코드Design What You Desire: Icon Generation from Orthogonal Application and Theme Labels
https://github.com/aria-zhangjl/e3-facenet
공식 코드https://github.com/arin-upna/biased-heritage
공식 코드Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
https://github.com/arkel23/animesion
공식 코드https://github.com/arnesh2212/SocialDF
공식 코드https://github.com/arpitbansal297/universal-guided-diffusion
공식 코드https://github.com/arturjordao/IncrementalDimensionalityReduction
공식 코드Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method
https://github.com/aryan-garg/styledcgan
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.