Date of Award
12-2017
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering (Holcomb Dept. of)
Committee Member
Dr. Melissa C. Smith, Committee Chair
Committee Member
Dr. Walter B. Ligon III
Committee Member
Dr. Adam W. Hoover
Abstract
Regardless of the Deep Learning community's continuous advancements, the challenging domain of one-shot learning still persists. While the human brain is capable of learning a new visual concept with ease, sometimes even at a glance, Deep Learning-based techniques show serious drawbacks in handling problems with small datasets. Much of the existing work on one-shot learning employs a variety of sophisticated network algorithms, prior domain knowledge, and data manipulation to address the generalization challenges presented in such problems. In this work, we demonstrate a one-shot learning method that contains three learning networks — a deep sequential generative model, a candidate network, and a Matching Network — thus offering an alternative approach to solving the one-shot classification problem. The proposed framework does not require domain knowledge, making it potentially portable to other domains. We show that our algorithm improves accuracy from 95.5% to 96.1% on the Omniglot dataset 20-way one-shot learning compared to current state-of-the-art.
Recommended Citation
Guo, Hanyu, "One-shot Learning In Deep Sequential Generative Models" (2017). All Theses. 2792.
https://tigerprints.clemson.edu/all_theses/2792