Can Data Scientists Trick Deep Machine Learning Algorithms? | Machine Learning - Analytics Insight

Fine-tuning deep learning models just got easier with the black box adversarial reprogramming (BAR) technique, as Kamalika Some, Content Manager reports.

Photo: Machine Learning

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...

To bridge this gap raised, black-box adversarial reprogramming (BAR), addresses to reprogram a deployed ML model for black-box transfer learning through black-box setting and taking data scarcity and resource constraint into consideration.
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Source: Analytics Insight