https://github.com/KarimArmanious/Age-Net
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation
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.
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation
FaceNet: A Unified Embedding for Face Recognition and Clustering
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
FaceNet: A Unified Embedding for Face Recognition and Clustering
Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
FaceNet: A Unified Embedding for Face Recognition and Clustering
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
PCANet: A Simple Deep Learning Baseline for Image Classification?
Progressive Growing of GANs for Improved Quality, Stability, and Variation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation
FaceNet: A Unified Embedding for Face Recognition and Clustering
Very Deep Convolutional Networks for Large-Scale Image Recognition
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.