Incremental Task Learning with Incremental Rank Updates

By Rakib Hyder, Ke Shao, Boyu Hou, Panos P. Markopoulos, Ashley Prater-Bennette, M. Salman Asif. 2022

17 citations recorded by the source corpus.

Incremental Task learning (ITL) is a category of continual learning that seeks to train a single network for multiple tasks (one after another), where training data for each task is only available during the training of that task. Neural networks tend to forget older tasks when they are trained for the newer tasks; this property is often known as catastrophic forgetting. To address this issue, ITL methods use episodic memory, parameter regularization, masking and pruning, or extensible network structures. In this paper, we propose a new incremental task learning framework based on low-rank factorization. In particular, we represent the network weights for each layer as a linear combination of several rank-1 matrices. To update the network for a new task, we learn a rank-1 (or low-rank) matrix and add that to the weights of every layer. We also introduce an additional selector vector that assigns different weights to the low-rank matrices learned for the previous tasks. We show that our approach performs better than the current state-of-the-art methods in terms of accuracy and forgetting. Our method also offers better memory efficiency compared to episodic memory- and mask-based approaches. Our code will be available at https://github.com/CSIPlab/task-increment-rank-update.git

Subfields: Efficiency & Compression, Meta & Continual Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Efficiency & Compression

  1. Liyuan Wang
  2. Jun Zhu
  3. Hang Su
  4. Xingxing Zhang

In Meta & Continual Learning

  1. Xingxing Zhang
  2. Liyuan Wang
  3. Hang Su
  4. Jun Zhu
  5. Simone Scardapane
  6. Valerio Marsocci
  7. Liyuan Wang
  8. Hongwei Yan

Citations in the corpus