Recent works onadversarial reprogramming have shown that it is possible to repurpose neuralnetworks for alternate tasks without modifying the network architecture or parameters . In this work, we broaden the scope of adversarialreprogramming beyond the data modality of the original task . We analyze thefeasibility of adversarially repurposing image classification neural networks for Natural Language Processing (NLP) and other sequence classification tasks . We demonstrate that by using highly efficient adversarialprograms, we can reprogram image classifiers to achieve competitive performance on a variety of text and sequence classification benchmarks without retraining the network . We use an efficient program that maps a sequence of discretetokens

Author(s) : Paarth Neekhara, Shehzeen Hussain, Jinglong Du, Shlomo Dubnov, Farinaz Koushanfar, Julian McAuley

Links : PDF - Abstract

Code :

https://github.com/oktantod/RoboND-DeepLearning-Project


Coursera

Keywords : sequence - classification - reprogramming - image - tasks -

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