https://github.com/pablovin/AffectiveMemoryFramework
공식 코드The FaceChannel: A Light-weight Deep Neural Network for Facial Expression 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.
The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition
Darker than Black-Box: Face Reconstruction from Similarity Queries
Enhancing Facial Privacy Protection via Weakening Diffusion Purification
Training Debiased Subnetworks with Contrastive Weight Pruning
Comparative Analysis of Audio Feature Extraction for Real-Time Talking Portrait Synthesis
Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms
Transferable Adversarial Face Attack with Text Controlled Attribute
Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection
Defense Against Adversarial Attacks on Audio DeepFake Detection
SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection
Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
MRI-GAN: A Generalized Approach to Detect DeepFakes using Perceptual Image Assessment
A Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition Tasks
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
Analyzing Fairness in Deepfake Detection With Massively Annotated Databases
Pixel-Level Face Image Quality Assessment for Explainable Face Recognition
Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
MAAD-Face: A Massively Annotated Attribute Dataset for Face Images
Unsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition
QMagFace: Simple and Accurate Quality-Aware Face Recognition
AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark
Preserving AUC Fairness in Learning with Noisy Protected Groups
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.