Self-Supervised Metric-Based Meta-Learning for Few-Shot Image classification
In this work, metric-based meta-learning models are proposed to learn a generic model embedding that can reduce the data shifting effect and thereby effectively distinguish the unseen samples. In addition, self-supervised learning is employed to mitigate the data scarcity problem by learning a robus...
Saved in:
Main Author: | Lim, Jit Yan |
---|---|
Format: | Thesis |
Published: |
2022
|
Subjects: | |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An improved algorithm for iris classification by using support vector machine and binary random machine learning
by: Kamarulzalis, Ahmad Haadzal
Published: (2018) -
Hybrid Computational Intelligence Models With Symbolic Rule Extraction For Pattern Classification
by: Quteishat, Anas Mohammad Ali
Published: (2008) -
Text to image synthesis using generative adversarial network
by: Tan, Yong Xuan
Published: (2022) -
Deep learning for face detection using matlab
by: Slim, Salim Adnan
Published: (2020) -
Anomaly prediction in electricity consumption using Machine Learning Techniques
by: Saber Mohammed Hassan Elhadad, Rawan Mohammed
Published: (2023)