70,290 research outputs found
Domain-Aware Augmentations for Unsupervised Online General Continual Learning
Continual Learning has been challenging, especially when dealing with
unsupervised scenarios such as Unsupervised Online General Continual Learning
(UOGCL), where the learning agent has no prior knowledge of class boundaries or
task change information. While previous research has focused on reducing
forgetting in supervised setups, recent studies have shown that self-supervised
learners are more resilient to forgetting. This paper proposes a novel approach
that enhances memory usage for contrastive learning in UOGCL by defining and
using stream-dependent data augmentations together with some implementation
tricks. Our proposed method is simple yet effective, achieves state-of-the-art
results compared to other unsupervised approaches in all considered setups, and
reduces the gap between supervised and unsupervised continual learning. Our
domain-aware augmentation procedure can be adapted to other replay-based
methods, making it a promising strategy for continual learning.Comment: Accepted to BMVC'2
Learning Representations on the Unit Sphere: Application to Online Continual Learning
We use the maximum a posteriori estimation principle for learning
representations distributed on the unit sphere. We derive loss functions for
the von Mises-Fisher distribution and the angular Gaussian distribution, both
designed for modeling symmetric directional data. A noteworthy feature of our
approach is that the learned representations are pushed toward fixed
directions, allowing for a learning strategy that is resilient to data drift.
This makes it suitable for online continual learning, which is the problem of
training neural networks on a continuous data stream, where multiple
classification tasks are presented sequentially so that data from past tasks
are no longer accessible, and data from the current task can be seen only once.
To address this challenging scenario, we propose a memory-based representation
learning technique equipped with our new loss functions. Our approach does not
require negative data or knowledge of task boundaries and performs well with
smaller batch sizes while being computationally efficient. We demonstrate with
extensive experiments that the proposed method outperforms the current
state-of-the-art methods on both standard evaluation scenarios and realistic
scenarios with blurry task boundaries. For reproducibility, we use the same
training pipeline for every compared method and share the code at
https://t.ly/SQTj.Comment: 16 pages, 4 figures, under revie
- …