Few-Shot Learning for Image Classification of Common Flora

by   Joshua Ball, et al.

The use of meta-learning and transfer learning in the task of few-shot image classification is a well researched area with many papers showcasing the advantages of transfer learning over meta-learning in cases where data is plentiful and there is no major limitations to computational resources. In this paper we will showcase our experimental results from testing various state-of-the-art transfer learning weights and architectures versus similar state-of-the-art works in the meta-learning field for image classification utilizing Model-Agnostic Meta Learning (MAML). Our results show that both practices provide adequate performance when the dataset is sufficiently large, but that they both also struggle when data sparsity is introduced to maintain sufficient performance. This problem is moderately reduced with the use of image augmentation and the fine-tuning of hyperparameters. In this paper we will discuss: (1) our process of developing a robust multi-class convolutional neural network (CNN) for the task of few-shot image classification, (2) demonstrate that transfer learning is the superior method of helping create an image classification model when the dataset is large and (3) that MAML outperforms transfer learning in the case where data is very limited. The code is available here: github.com/JBall1/Few-Shot-Limited-Data


page 1

page 4

page 5

page 7

page 8

page 9


A Meta-Learning Approach for Custom Model Training

Transfer-learning and meta-learning are two effective methods to apply k...

Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier

Recent papers have suggested that transfer learning can outperform sophi...

Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification

We introduce Meta-Album, an image classification meta-dataset designed t...

TIML: Task-Informed Meta-Learning for Agriculture

Labeled datasets for agriculture are extremely spatially imbalanced. Whe...

Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes

The goal of this paper is to design image classification systems that, a...

CRL: Class Representative Learning for Image Classification

Building robust and real-time classifiers with diverse datasets are one ...

Please sign up or login with your details

Forgot password? Click here to reset