DeepAI AI Chat
Log In Sign Up

Transfer Learning via Test-Time Neural Networks Aggregation

by   Bruno Casella, et al.
Edinburgh Napier University
Università di Torino
University of Catania

It has been demonstrated that deep neural networks outperform traditional machine learning. However, deep networks lack generalisability, that is, they will not perform as good as in a new (testing) set drawn from a different distribution due to the domain shift. In order to tackle this known issue, several transfer learning approaches have been proposed, where the knowledge of a trained model is transferred into another to improve performance with different data. However, most of these approaches require additional training steps, or they suffer from catastrophic forgetting that occurs when a trained model has overwritten previously learnt knowledge. We address both problems with a novel transfer learning approach that uses network aggregation. We train dataset-specific networks together with an aggregation network in a unified framework. The loss function includes two main components: a task-specific loss (such as cross-entropy) and an aggregation loss. The proposed aggregation loss allows our model to learn how trained deep network parameters can be aggregated with an aggregation operator. We demonstrate that the proposed approach learns model aggregation at test time without any further training step, reducing the burden of transfer learning to a simple arithmetical operation. The proposed approach achieves comparable performance w.r.t. the baseline. Besides, if the aggregation operator has an inverse, we will show that our model also inherently allows for selective forgetting, i.e., the aggregated model can forget one of the datasets it was trained on, retaining information on the others.


Disposable Transfer Learning for Selective Source Task Unlearning

Transfer learning is widely used for training deep neural networks (DNN)...

EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning

Deep transfer learning techniques try to tackle the limitations of deep ...

Learning Curves for Sequential Training of Neural Networks: Self-Knowledge Transfer and Forgetting

Sequential training from task to task is becoming one of the major objec...

Transfer learning approach for financial applications

Artificial neural networks learn how to solve new problems through a com...

A Unified Framework for Lifelong Learning in Deep Neural Networks

Humans can learn a variety of concepts and skills incrementally over the...

Deep Transfer Learning for Multiple Class Novelty Detection

We propose a transfer learning-based solution for the problem of multipl...

Deep Combinatorial Aggregation

Neural networks are known to produce poor uncertainty estimations, and a...