Keras code for the paper: Cognitively-Inspired Model for Incremental Learning Using a Few Examples

Incremental learning attempts to develop a classifier which learns continuously from a stream of data segregated into different classes. Deep learning approaches suffer from catastrophic forgetting when learning classes incrementally, while most incremental learning approaches require a large amount of training data per class. We examine the problem of incremental learning using only a few training examples, referred to as Few-Shot Incremental Learning (FSIL). To solve this problem, we propose a novel approach inspired by the concept learning model of the hippocampus and the neocortex that represents each image class as centroids and does not suffer from catastrophic forgetting. We evaluate our approach on three class-incremental learning benchmarks: Caltech-101, CUBS-200-2011 and CIFAR-100 for incremental and few-shot incremental learning and show that our approach achieves state-of-the-art results in terms of classification accuracy over all learned classes.

• Keras (Version 2.1.6)
• Scipy (Currently working with 1.2.1)
• Scikit Learn (Currently working with 0.21.2)
• if you can't find a pre-trained model in Kears, get them from https://github.com/qubvel/classification_models
• Download the datasets in */data directory

• First run get_features.py to get the ResNet features for all the images in the dataset. For Caltheh-101 and CUBS-200-2011 use ResNet-18 instead of ResNet-34.
• After feature extraction, simply run main_file.py to get the results for all increments.

@InProceedings{Ayub_2020_CVPR_Workshops,  
author = {Ayub, Ali and Wagner, Alan R.},  
title = {Cognitively-Inspired Model for Incremental Learning Using a Few Examples},  
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},  
month = {June},  
year = {2020}  
}