https://github.com/Tools4Project/4501Project
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
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
FaceNet: A Unified Embedding for Face Recognition and Clustering
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
Federated Learning for Face Recognition with Gradient Correction
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
FastReID: A Pytorch Toolbox for General Instance Re-identification
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
RetinaFace: Single-stage Dense Face Localisation in the Wild
Teacher-Student Asynchronous Learning with Multi-Source Consistency for Facial Landmark Detection
Learning to Impute: A General Framework for Semi-supervised Learning
VGGFace2: A dataset for recognising faces across pose and age
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.