Factorized Tensor Networks for Multi-Task and Multi-Domain Learning
Yash Garg Nebiyou Yismaw Rakib Hyder Ashley Prater-Bennette M. Salman Asif
Multi-task and multi-domain learning methods seek to learn multiple
tasks/domains, jointly or one after another, using a single unified network.
The key challenge and opportunity is to exploit shared information across tasks
and domains to improve the efficiency of the unified network. The efficiency
can be in terms of accuracy, storage cost, computation, or sample complexity.
In this paper, we propose a factorized tensor network (FTN) that can achieve
accuracy comparable to independent single-task/domain networks with a small
number of additional parameters. FTN uses a frozen backbone network from a
source model and incrementally adds task/domain-specific low-rank tensor
factors to the shared frozen network. This approach can adapt to a large number
of target domains and tasks without catastrophic forgetting. Furthermore, FTN
requires a significantly smaller number of task-specific parameters compared to
existing methods. We performed experiments on widely used multi-domain and
multi-task datasets. We show the experiments on convolutional-based
architecture with different backbones and on transformer-based architecture. We
observed that FTN achieves similar accuracy as single-task/domain methods while
using only a fraction of additional parameters per task.