3D Face Modelling
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jmpu/webconf21-deepfakes-in-the-wild
공식 코드https://github.com/joelb92/ND_Frontalization_Project
공식 코드https://github.com/joelouismarino/variational_rl
공식 코드https://github.com/johannesack/offlinerlstructurednonstationarity
공식 코드Offline Reinforcement Learning from Datasets with Structured Non-Stationarity
https://github.com/jongli747/curricular-sincnet
공식 코드https://github.com/joojs/fairface
공식 코드FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
https://github.com/josephdrahos/sreds
공식 코드https://github.com/jqueeney/gpi
공식 코드Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse
https://github.com/jqueeney/robust-safe-rl
공식 코드Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees
https://github.com/jren03/garage
공식 코드https://github.com/juliojj/app-real-age
공식 코드On the effect of age perception biases for real age regression
https://github.com/junfanlin/continuous-transition
공식 코드Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp
https://github.com/jung-jun-uk/mixface
공식 코드MixFace: Improving Face Verification Focusing on Fine-grained Conditions
https://github.com/jung-jun-uk/unpg
공식 코드Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition
https://github.com/jungyg/alsuv
공식 코드Face Reconstruction Transfer Attack as Out-of-Distribution Generalization
https://github.com/jungyu0413/Navigating-Label-Ambiguity-NLA-
공식 코드Navigating Label Ambiguity for Facial Expression Recognition in the Wild
https://github.com/jungyu0413/V-NAW
공식 코드V-NAW: Video-based Noise-aware Adaptive Weighting for Facial Expression Recognition
https://github.com/junjun-jiang/Face-Hallucination-Benchmark
공식 코드https://github.com/junjun-jiang/TLcR-RL
공식 코드https://github.com/junshutang/3DFaceShop
공식 코드3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation
https://github.com/junwan2014/MMDN-master
공식 코드Robust Facial Landmark Detection by Multi-order Multi-constraint Deep Networks
https://github.com/justusschock/shapenet
공식 코드https://github.com/jymoon0613/simfle
공식 코드https://github.com/kacperkan/conerf
공식 코드https://github.com/kaiwang960112/Challenge-condition-FER-dataset
공식 코드Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
https://github.com/kaiwang960112/Self-Cure-Network
공식 코드Suppressing Uncertainties for Large-Scale Facial Expression Recognition
https://github.com/kaiyangzhou/coop
공식 코드https://github.com/kallekku/cmcgs
공식 코드https://github.com/kalyo-zjl/WM3DR
공식 코드https://github.com/kangyeolk/animeceleb
공식 코드AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment
https://github.com/kartik-3004/segface
공식 코드https://github.com/kdhht2334/elim_fer
공식 코드Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
https://github.com/keivanB/Young_Face_Aging_YFA
공식 코드https://github.com/keraJLi/synthetic-gymnax
공식 코드https://github.com/kevinhuang8/cem-gd
공식 코드CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.