https://github.com/Puzer/stylegan-encoder
Very Deep Convolutional Networks for Large-Scale Image 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.
Very Deep Convolutional Networks for Large-Scale Image Recognition
RetinaFace: Single-stage Dense Face Localisation in the Wild
RetinaFace: Single-stage Dense Face Localisation in the Wild
RetinaFace: Single-stage Dense Face Localisation in the Wild
FaceNet: A Unified Embedding for Face Recognition and Clustering
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings
Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
FaceNet: A Unified Embedding for Face Recognition and Clustering
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
High-Resolution Representations for Labeling Pixels and Regions
Very Deep Convolutional Networks for Large-Scale Image Recognition
RetinaFace: Single-stage Dense Face Localisation in the Wild
Deep Learning Face Representation by Joint Identification-Verification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.