Global Big Data Conference

Fine-tuning deep learning models just got easier with the black box adversarial reprogramming (BAR) technique. When data scientists mention AI and machine learning models, the hot topic of discussion always revolves around not having enough training samples to fine-tune the deep learning models. Consequently, they rely on transfer learning to subsequently fine-tune pre-train deep learning models to increase a model's accuracy. To make data scientists work a lot easier, at the International Conference on Machine Learning (ICML) scientists at IBM research and Taiwan's National Tsing Hua University unrevealed the Black Box Adversarial Reprogramming (BAR) touted as an alternative repurposing technique which turns the weakness of deep neural networks into a strength. Explaining BAR, the research paper presented at the paper read, "Black Box Adversarial Reprogramming repurposes a well-trained black-box ML model 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".