https://github.com/hengruilou/deepfacegen
공식 코드A Large-scale Universal Evaluation Benchmark For Face Forgery Detection
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A Large-scale Universal Evaluation Benchmark For Face Forgery Detection
Dynamic Multi-Task Learning for Face Recognition with Facial Expression
Dynamic Deep Multi-task Learning for Caricature-Visual Face Recognition
Distributionally Generative Augmentation for Fair Facial Attribute Classification
Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement Learning
Long-Term Face Tracking for Crowded Video-Surveillance Scenarios
Exploring Spatial-Temporal Features for Deepfake Detection and Localization
Attention-guided Progressive Mapping for Profile Face Recognition
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning
TAAC: Temporally Abstract Actor-Critic for Continuous Control
Optimistic Curiosity Exploration and Conservative Exploitation with Linear Reward Shaping
DefakeHop: A Light-Weight High-Performance Deepfake Detector
Compensation Sampling for Improved Convergence in Diffusion Models
DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment
SeqFace: Make full use of sequence information for face recognition
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
Attributes Aware Face Generation with Generative Adversarial Networks
PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting
3D Cartoon Face Generation with Controllable Expressions from a Single GAN Image
DiffFace: Diffusion-based Face Swapping with Facial Guidance
InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition
A real-time and unsupervised face Re-Identification system for Human-Robot Interaction
FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild
Action Robust Reinforcement Learning and Applications in Continuous Control
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.