Domain adaption for few-shot learning in image classification
Machine learning has been widely used in various fields and successfully addresses many image classification problems in the presence of sufficient samples, but performs poorly in the absence of samples. Few-shot learning is an innovative approach to solving this problem. In this article, I con...
Saved in:
Main Author: | Pan, Yifei |
---|---|
Other Authors: | Mao Kezhi |
Format: | Thesis-Master by Coursework |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/164914 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Adaptive deep few-shot learning
by: Gu, Rong
Published: (2023) -
Disentangled feature representation for few-shot image classification
by: Cheng, Hao, et al.
Published: (2023) -
Semantic-guided distribution calibrating for few-shot classification
by: Qiu, Guochen
Published: (2022) -
Visual food recognition using few-shot learning
by: Liu, Tianyi
Published: (2020) -
Low-shot machine learning for medical image classification
by: Yip, Chun Mun
Published: (2020)