6 research outputs found
Preserving Linear Separability in Continual Learning by Backward Feature Projection
Catastrophic forgetting has been a major challenge in continual learning,
where the model needs to learn new tasks with limited or no access to data from
previously seen tasks. To tackle this challenge, methods based on knowledge
distillation in feature space have been proposed and shown to reduce
forgetting. However, most feature distillation methods directly constrain the
new features to match the old ones, overlooking the need for plasticity. To
achieve a better stability-plasticity trade-off, we propose Backward Feature
Projection (BFP), a method for continual learning that allows the new features
to change up to a learnable linear transformation of the old features. BFP
preserves the linear separability of the old classes while allowing the
emergence of new feature directions to accommodate new classes. BFP can be
integrated with existing experience replay methods and boost performance by a
significant margin. We also demonstrate that BFP helps learn a better
representation space, in which linear separability is well preserved during
continual learning and linear probing achieves high classification accuracy.
The code can be found at https://github.com/rvl-lab-utoronto/BFPComment: CVPR 2023. The code can be found at
https://github.com/rvl-lab-utoronto/BF
MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning
Recently, there has been an increasing interest in automated prompt
optimization based on reinforcement learning (RL). This approach offers
important advantages, such as generating interpretable prompts and being
compatible with black-box foundation models. However, the substantial prompt
space size poses challenges for RL-based methods, often leading to suboptimal
policy convergence. This paper introduces MultiPrompter, a new framework that
views prompt optimization as a cooperative game between prompters which take
turns composing a prompt together. Our cooperative prompt optimization
effectively reduces the problem size and helps prompters learn optimal prompts.
We test our method on the text-to-image task and show its ability to generate
higher-quality images than baselines
Online Class-Incremental Continual Learning with Adversarial Shapley Value
As image-based deep learning becomes pervasive on every device, from cell
phones to smart watches, there is a growing need to develop methods that
continually learn from data while minimizing memory footprint and power
consumption. While memory replay techniques have shown exceptional promise for
this task of continual learning, the best method for selecting which buffered
images to replay is still an open question. In this paper, we specifically
focus on the online class-incremental setting where a model needs to learn new
classes continually from an online data stream. To this end, we contribute a
novel Adversarial Shapley value scoring method that scores memory data samples
according to their ability to preserve latent decision boundaries for
previously observed classes (to maintain learning stability and avoid
forgetting) while interfering with latent decision boundaries of current
classes being learned (to encourage plasticity and optimal learning of new
class boundaries). Overall, we observe that our proposed ASER method provides
competitive or improved performance compared to state-of-the-art replay-based
continual learning methods on a variety of datasets.Comment: Proceedings of the 35th AAAI Conference on Artificial Intelligence
(AAAI-21
Dynamics of Serum Retinol and Alpha-Tocopherol Levels According to Non-Alcoholic Fatty Liver Disease Status
The available data on the association between micronutrients in the blood and non-alcoholic fatty liver disease (NAFLD) are limited. To investigate the clinical implications of this relationship, we sought to identify the difference in the serum levels of vitamins A and E according to NAFLD status using data from the seventh Korea National Health and Nutrition Examination Survey. In this cross-sectional study of the Korean population, NAFLD and its severity were defined using prediction models. Differences in the prevalence and severity of NAFLD were analyzed according to serum retinol (vitamin A) and alpha (α)-tocopherol (vitamin E) levels. Serum levels of retinol and α-tocopherol were positively correlated with the prevalence of NAFLD. In most prediction models of the NAFLD subjects, serum retinol deficiency was significantly correlated with advanced fibrosis, while serum α-tocopherol levels did not differ between individuals with or without advanced fibrosis. Similar trends were also noted with cholesterol-adjusted levels of α-tocopherol. In summary, while circulating concentrations of retinol and α-tocopherol were positively associated with the presence of NAFLD, advanced liver fibrosis was only correlated with serum retinol levels. Our findings could provide insight into NAFLD patient care at a micronutrient level