Few-Shot Learning for Online Large Scale Fine-Grained Image Recognition

In this work we have analyzed the existing body of research on few-shot learning and the various methods used to solve the few-shot image classification problem.
We have treated, both theoretically and experimentally, embedding learning methods, multitask methods, different kinds of metric losses. Tried various self- and semi-supervised learning methods like MoCo, SwAV, SimCLR, BYOL and SimSiam.
In addition, we have tackled the problem of performing online large-slace image
recognition. It requires solving tasks like Out-Of-Distribution detection and clusterization, class discovery.
As a result of this work, we have proposed a mechanism for implementing a
system to solve the problem of fine-grained large-scale image recognition using
few-shot learning methods.
We have conducted thourough qualitative and quantitative analysis on the application of various few-shot learning methods for image classification implemented in PyTorch both in benchmark and real-world data.
This methods will further be improved on and tested on real-world product data.

Holubakha M. I. Few-Shot Learning for Online Large Scale Fine-Grained Image Recognition : qualification work ... master’s : 122 Computer Science / Holubakha Mykyta Ihorovich. - Kyiv, 2021. - 58 p.