https://github.com/vgangal101/VGG_models
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
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
Very Deep Convolutional Networks for Large-Scale Image Recognition
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Detecting Faces Using Region-based Fully Convolutional Networks
FaceNet: A Unified Embedding for Face Recognition and Clustering
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
FaceNet: A Unified Embedding for Face Recognition and Clustering
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
GANimation: Anatomically-aware Facial Animation from a Single Image
High-Resolution Representations for Labeling Pixels and Regions
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Very Deep Convolutional Networks for Large-Scale Image Recognition
Deep High-Resolution Representation Learning for Visual Recognition
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.