Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels

TL;DR

This paper introduces TIRTL, a novel learning approach that exploits task-irrelevant features to improve classification, suppressing negative transfer effects and complementing existing task-relevant transfer methods.

Contribution

It proposes a new method for leveraging task-irrelevant features from labels, providing a theoretical basis and demonstrating effectiveness in facial and digit recognition tasks.

Findings

Improved classification accuracy on facial expression recognition.

Effective suppression of negative transfer from irrelevant features.

Compatibility with existing task-relevant transfer methods.

Abstract

Deep neural models have hitherto achieved significant performances on numerous classification tasks, but meanwhile require sufficient manually annotated data. Since it is extremely time-consuming and expensive to annotate adequate data for each classification task, learning an empirically effective model with generalization on small dataset has received increased attention. Existing efforts mainly focus on transferring task-relevant knowledge from other similar data to tackle the issue. These approaches have yielded remarkable improvements, yet neglecting the fact that the task-irrelevant features could bring out massive negative transfer effects. To date, no large-scale studies have been performed to investigate the impact of task-irrelevant features, let alone the utilization of this kind of features. In this paper, we firstly propose Task-Irrelevant Transfer Learning (TIRTL) to exploit task-irrelevant features, which mainly are extracted from task-irrelevant labels. Particularly, we suppress the expression of task-irrelevant information and facilitate the learning process of classification. We also provide a theoretical explanation of our method. In addition, TIRTL does not conflict with those that have previously exploited task-relevant knowledge and can be well combined to enable the simultaneous utilization of task-relevant and task-irrelevant features for the first time. In order to verify the effectiveness of our theory and method, we conduct extensive experiments on facial expression recognition and digit recognition tasks. Our source code will be also available in the future for reproducibility.