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/rafaelmmamede/fairness_ocfr
공식 코드Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition
https://github.com/rafaelvareto/maximal-entropy-loss
공식 코드Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
https://github.com/raining-dev/avt2-dwf
공식 코드AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies
https://github.com/rainy-xu/tall4deepfake
공식 코드https://github.com/rama0126/pwtf-dvd
공식 코드Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection
https://github.com/ramihaf/mtf_data_set
공식 코드https://github.com/raphaelmemmesheimer/sl-dml
공식 코드SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition
https://github.com/raywang335/l2rclip
공식 코드https://github.com/razla/xai-based-detection-of-adversarial-attacks-on-deepfake-detectors
공식 코드XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
https://github.com/realityengines/post_hoc_debiasing
공식 코드https://github.com/recluzegeek/AWARE-NET
공식 코드AWARE-NET: Adaptive Weighted Averaging for Robust Ensemble Network in Deepfake Detection
https://github.com/redcping/Delta_Age_AdaIN
공식 코드DAA: A Delta Age AdaIN operation for age estimation via binary code transformer
https://github.com/renderme-360/renderme-360
공식 코드RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
https://github.com/renmin1991/perturbation-inactivate
공식 코드Perturbation Inactivation Based Adversarial Defense for Face Recognition
https://github.com/rgb91/temporal-deepfake-segmentation
공식 코드https://github.com/riaduli/thresholded_event_vision_face_dataset
공식 코드Descriptor: Face Detection Dataset for Programmable Threshold-Based Sparse-Vision
https://github.com/ristea/xmad-bench
공식 코드XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark
https://github.com/rizhaocai/metapattern_fas
공식 코드https://github.com/rlit/LatentRANSAC
공식 코드https://github.com/rmushfiqur2/deepfake_op_rel
공식 코드Individualized Deepfake Detection Exploiting Traces Due to Double Neural-Network Operations
https://github.com/rohdelab/drcdt_face
공식 코드Local Sliced-Wasserstein Feature Sets for Illumination-invariant Face Recognition
https://github.com/rohit7044/3D-GANTex
공식 코드https://github.com/ronny3050/PrimateFaceRecognition
공식 코드https://github.com/roscibely/Gender-Classification
공식 코드Predicting Gender by First Name Using Character-level Machine Learning
https://github.com/royorel/FFHQ-Aging-Dataset
공식 코드https://github.com/royorel/Lifespan_Age_Transformation_Synthesis
공식 코드https://github.com/royorel/StyleSDF
공식 코드StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation
https://github.com/rperi/trustworthy-asv-fairness
공식 코드https://github.com/rshaojimmy/AAAI2020-RFMetaFAS
공식 코드https://github.com/rshaojimmy/deepfake-adapter
공식 코드https://github.com/rshaojimmy/multimodal-deepfake
공식 코드https://github.com/rshaojimmy/seqdeepfake
공식 코드https://github.com/rub-syssec/generatedmediasurvey
공식 코드A Representative Study on Human Detection of Artificially Generated Media Across Countries
https://github.com/rub-syssec/wavefake
공식 코드https://github.com/ruijiezhu94/ti-face
공식 코드TIFace: Improving Facial Reconstruction through Tensorial Radiance Fields and Implicit Surfaces
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.