A primary focus area in continual learning research is alleviating the" catastrophic forgetting"
problem in neural networks by designing new algorithms that are more robust to the …

A large body of research in continual learning is devoted to overcoming the catastrophic
forgetting of neural networks by designing new algorithms that are robust to the distribution …

It is often useful to compactly summarize important properties of model parameters and
training data so that they can be used later without storing and/or iterating over the entire …

We present the One Pass ImageNet (OPIN) problem, which aims to study the effectiveness
of deep learning in a streaming setting. ImageNet is a widely known benchmark dataset that …

A growing body of research in continual learning focuses on the catastrophic forgetting
problem. While many attempts have been made to alleviate this problem, the majority of the …

The goal of lifelong learning is to continuously learn from non-stationary distributions where
the non-stationarity is typically imposed by a sequence of distinct tasks. Prior works have …

To adapt to real-world dynamics, intelligent systems need to assimilate new knowledge
without catastrophic forgetting, where learning new tasks leads to a degradation in …

Task-free continual learning is the subfield of machine learning that focuses on learning
online from a stream whose distribution changes continuously over time. In contrast …

Contemporary deep neural networks offer state-of-the-art results when applied to visual
reasoning, eg, in the context of 3D point cloud data. Point clouds are an important data type …

When deep-learning classifiers try to learn new classes through supervised learning, they
exhibit catastrophic forgetting issues. In this paper we propose the Gaussian Mixture Model …