Semantic-guided distribution calibrating for few-shot classification

• المؤلف الرئيسي: Qiu, Guochen
• مؤلفون آخرون: Wen Bihan
• التنسيق: Thesis-Master by Coursework
• اللغة: English
• منشور في: Nanyang Technological University 2022
• الموضوعات: Engineering::Electrical and electronic engineering
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/155041

The meta-learning paradigm is a mainstream method to solve the problem of few-shot learning. It constructs multiple tasks through episodic training so that the model can quickly generalize to new tasks. However, episodic training only sample a few training instances (shots) per class for simulating...