Current transfer learning methods are mainly based on finetuning a pretrained
model with target-domain data. Motivated by the techniques from adversarial
machine learning (ML) that are capable of manipulating the model prediction via
data perturbations, in this paper we propose a novel approach, black-box
adversarial reprogramming (BAR), that repurposes a well-trained black-box ML
model (e.g., a prediction API or a proprietary software) for solving different
ML tasks, especially in the scenario with scarce data and constrained
resources. The rationale lies in exploiting high-performance but unknown ML
models to gain learning capability for transfer learning. Using zeroth order
optimization and multi-label mapping techniques, BAR can reprogram a black-box
ML model solely based on its input-output responses without knowing the model
architecture or changing any parameter. More importantly, in the limited
medical data setting, on autism spectrum disorder classification, diabetic
retinopathy detection, and melanoma detection tasks, BAR outperforms
state-of-the-art methods and yields comparable performance to the vanilla
adversarial reprogramming method requiring complete knowledge of the target ML
model. BAR also outperforms baseline transfer learning approaches by a
significant margin, demonstrating cost-effective means and new insights for
transfer learning.

本文提出了一种黑盒对抗重新编程 (BAR) 的新方法来解决转移学习中的数据稀缺和资源受限等问题，该方法利用高性能但未知的机器学习模型来获得学习能力，并在自闭症谱系障碍分类、糖尿病视网膜病变检测和黑色素瘤检测任务中优于现有技术和基线模型，展示了转移学习的新思路和成本效益手段。