https://github.com/ergastialex/ldm-diffusion-sem
공식 코드Controllable Face Synthesis with Semantic Latent Diffusion Models
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Controllable Face Synthesis with Semantic Latent Diffusion Models
Controllable Face Synthesis with Semantic Latent Diffusion Models
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Deepfake Video Detection Using Convolutional Vision Transformer
Deepfake Video Detection Using Generative Convolutional Vision Transformer
Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection
Raw Differentiable Architecture Search for Speech Deepfake and Spoofing Detection
2D-Malafide: Adversarial Attacks Against Face Deepfake Detection Systems
Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories
Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking
FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction
Stabilizing Unsupervised Environment Design with a Learned Adversary
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping
Masked Trajectory Models for Prediction, Representation, and Control
MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control
On the model-based stochastic value gradient for continuous reinforcement learning
CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search
Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priors
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods
RMES: Real-Time Micro-Expression Spotting Using Phase From Riesz Pyramid
Domain-Specific Face Synthesis for Video Face Recognition from a Single Sample Per Person
Harmonizing Flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.