https://github.com/alililia/ms_contrib/tree/main/ascend_vgg19
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
Very Deep Convolutional Networks for Large-Scale Image Recognition
RetinaFace: Single-stage Dense Face Localisation in the Wild
Learning from Failure: Training Debiased Classifier from Biased Classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
Unsupervised learning of object landmarks by factorized spatial embeddings
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation
FaceNet: A Unified Embedding for Face Recognition and Clustering
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.