Continuous/Lifelong learning of high-dimensional data streams is a challenging research problem. In fact, fully retraining models each time new data become available is infeasible, due to computational and storage issues, while na\"ive incremental strategies have been shown to suffer from catastrophic forgetting. In the context of real-world object recognition applications (e.g., robotic vision), where continuous learning is crucial, very few datasets and benchmarks are available to evaluate and compare emerging techniques. In this work we propose a new dataset and benchmark CORe50, specifically designed for continuous object recognition, and introduce baseline approaches for different continuous learning scenarios.
@article{arxiv.1705.03550,
title = {CORe50: a New Dataset and Benchmark for Continuous Object Recognition},
author = {Vincenzo Lomonaco and Davide Maltoni},
journal= {arXiv preprint arXiv:1705.03550},
year = {2017}
}