arXiv:2304.01023
Self-Supervised learning for Neural Architecture Search (NAS)
원문 초록
The objective of this internship is to propose an innovative method that uses unlabelled data, i.e. data that will allow the AI to automatically learn to predict the correct outcome. To reach this stage, the steps to be followed can be defined as follows: (1) consult the state of the art and position ourself against it, (2) come up with ideas for development paths, (3) implement these ideas, (4) and finally test them to position ourself against the state of the art, and then start the sequence again. During my internship, this sequence was done several times and therefore gives the tracks explored during the internship.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Multidimensional Particle Filter for Long-Term Visual Teach and Repeat in Changing Environments
Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
RetFiner: A Vision-Language Refinement Scheme for Retinal Foundation Models
Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features
Post-training for Deepfake Speech Detection
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition
토론
이 논문과 연결된 포럼 글이 아직 없습니다.