17 research outputs found
Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection
Continual learning for Semantic Segmentation (CSS) is a rapidly emerging
field, in which the capabilities of the segmentation model are incrementally
improved by learning new classes or new domains. A central challenge in
Continual Learning is overcoming the effects of catastrophic forgetting, which
refers to the sudden drop in accuracy on previously learned tasks after the
model is trained on new classes or domains. In continual classification this
challenge is often overcome by replaying a small selection of samples from
previous tasks, however replay is rarely considered in CSS. Therefore, we
investigate the influences of various replay strategies for semantic
segmentation and evaluate them in class- and domain-incremental settings. Our
findings suggest that in a class-incremental setting, it is critical to achieve
a uniform distribution for the different classes in the buffer to avoid a bias
towards newly learned classes. In the domain-incremental setting, it is most
effective to select buffer samples by uniformly sampling from the distribution
of learned feature representations or by choosing samples with median entropy.
Finally, we observe that the effective sampling methods help to decrease the
representation shift significantly in early layers, which is a major cause of
forgetting in domain-incremental learning.Comment: Accepted at 2022 IEEE Conference on Intelligent Transportation
Systems (ITSC 2022
Balanced Softmax Cross-Entropy for Incremental Learning
Deep neural networks are prone to catastrophic forgetting when incrementally
trained on new classes or new tasks as adaptation to the new data leads to a
drastic decrease of the performance on the old classes and tasks. By using a
small memory for rehearsal and knowledge distillation, recent methods have
proven to be effective to mitigate catastrophic forgetting. However due to the
limited size of the memory, large imbalance between the amount of data
available for the old and new classes still remains which results in a
deterioration of the overall accuracy of the model. To address this problem, we
propose the use of the Balanced Softmax Cross-Entropy loss and show that it can
be combined with exiting methods for incremental learning to improve their
performances while also decreasing the computational cost of the training
procedure in some cases. Complete experiments on the competitive ImageNet,
subImageNet and CIFAR100 datasets show states-of-the-art results
Hypothesis-driven Online Video Stream Learning with Augmented Memory
The ability to continuously acquire new knowledge without forgetting previous
tasks remains a challenging problem for computer vision systems. Standard
continual learning benchmarks focus on learning from static iid images in an
offline setting. Here, we examine a more challenging and realistic online
continual learning problem called online stream learning. Like humans, some AI
agents have to learn incrementally from a continuous temporal stream of
non-repeating data. We propose a novel model, Hypotheses-driven Augmented
Memory Network (HAMN), which efficiently consolidates previous knowledge using
an augmented memory matrix of "hypotheses" and replays reconstructed image
features to avoid catastrophic forgetting. Compared with pixel-level and
generative replay approaches, the advantages of HAMN are two-fold. First,
hypothesis-based knowledge consolidation avoids redundant information in the
image pixel space and makes memory usage far more efficient. Second, hypotheses
in the augmented memory can be re-used for learning new tasks, improving
generalization and transfer learning ability. Given a lack of online
incremental class learning datasets on video streams, we introduce and adapt
two additional video datasets, Toybox and iLab, for online stream learning. We
also evaluate our method on the CORe50 and online CIFAR100 datasets. Our method
performs significantly better than all state-of-the-art methods, while offering
much more efficient memory usage. All source code and data are publicly
available at https://github.com/kreimanlab/AugMe
How do Human Users Teach a Continual Learning Robot in Repeated Interactions?
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