https://github.com/eduardacaldeira/mst-kd
공식 코드MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition
Speech Driven Talking Face Generation from a Single Image and an Emotion Condition
Linear Disentangled Representation Learning for Facial Actions
Linear Disentangled Representation Learning for Facial Actions
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
Controllable Face Synthesis with Semantic Latent Diffusion Models
Controllable Face Synthesis with Semantic Latent Diffusion Models
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
FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction
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.