How to trick deep learning algorithms into doing new things

Two things often mentioned with deep learning are “data” and “compute resources.” You need a lot of both when developing, training, and testing deep learning models. When developers don’t have a lot of training samples or access to very powerful servers, they use transfer learning to finetune a pre-trained deep learning model for a new task. At this year’s ICML conference, scientists at IBM Research and Taiwan’s National Tsing Hua University Research introduced “black-box adversarial reprogramming” (BAR), an alternative repurposing technique that turns a supposed weakness of deep neural networks into a strength. BAR expands the original work on adversarial reprogramming and previous work on black-box adversarial attacks to make it possible to expand the capabilities of deep neural networks even when developers don’t have full access to the model. [Read: What is adversarial machine learning?] Pretrained and finetuned deep learning models When you want to develop an application that requires deep learning, one option is to create your own neural network from scratch and train it on available or curated examples. For instance, you can use ImageNet, a public dataset that contains more than 14 million labeled images. There is a problem, however. First, you must find the right…  


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