https://github.com/raguilar-f/Thermal-Face-Recognition-Using-Convolutional-Neural-Networks-and-Transfer-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Face reconstruction is the task of recovering the facial geometry of a face from an image.
<span style="color:grey; opacity: 0.6">( Image credit: Microsoft Deep3DFaceReconstruction )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Progressive Growing of GANs for Improved Quality, Stability, and Variation
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Transfer Learning for Improving Speech Emotion Classification Accuracy
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
DeXpression: Deep Convolutional Neural Network for Expression Recognition
RetinaFace: Single-stage Dense Face Localisation in the Wild
FaceNet: A Unified Embedding for Face Recognition and Clustering
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.