TATL: Task Agnostic Transfer Learning for Skin Attributes Detection

TL;DR

TATL introduces a task-agnostic transfer learning framework for skin attribute detection, leveraging an attribute-agnostic segmenter to improve performance and data efficiency across various neural architectures.

Contribution

The paper proposes a novel task-agnostic transfer learning framework that enhances skin attribute detection by using an attribute-agnostic segmenter, addressing data scarcity and improving performance.

Findings

Achieves state-of-the-art results on skin attribute benchmarks.

Works effectively across multiple neural network architectures.

Reduces model complexity and computational requirements.

Abstract

Existing skin attributes detection methods usually initialize with a pre-trained Imagenet network and then fine-tune on a medical target task. However, we argue that such approaches are suboptimal because medical datasets are largely different from ImageNet and often contain limited training samples. In this work, we propose \emph{Task Agnostic Transfer Learning (TATL)}, a novel framework motivated by dermatologists' behaviors in the skincare context. TATL learns an attribute-agnostic segmenter that detects lesion skin regions and then transfers this knowledge to a set of attribute-specific classifiers to detect each particular attribute. Since TATL's attribute-agnostic segmenter only detects skin attribute regions, it enjoys ample data from all attributes, allows transferring knowledge among features, and compensates for the lack of training data from rare attributes. We conduct extensive experiments to evaluate the proposed TATL transfer learning mechanism with various neural network architectures on two popular skin attributes detection benchmarks. The empirical results show that TATL not only works well with multiple architectures but also can achieve state-of-the-art performances while enjoying minimal model and computational complexities. We also provide theoretical insights and explanations for why our transfer learning framework performs well in practice.