Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources

Nov 23, 2020 1 min read

Black-box-Adversarial-Reprogramming

This is the repo for Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources, Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho, in Proceeding of International Conference on Machine Learning (ICML), 2020. Our code is implemented in Python 3.6 and Tensorflow 1.14.

The following figure illustrates the framework for our proposed black-box adversarial reprogramming method (BAR):

89761762-b2e55880-db21-11ea-93f8-db0cef7800c3

Generate adversarial program.
Find q pertubed adversarial programs with vectors that are uniformly drawn at random from a unit Euclidean sphere.
Estimate gradient with zeroth-order gradient estimator. The corresponding algorithmic convergence guarantees have been proved in both the convex loss and non-convex loss settings (Liu et al., 2018; 2019).
Optimize adversarial program’s parameters W.

GitHub

Machine Learning

John was the first writer to have joined pythonawesome.com. He has since then inculcated very effective writing and reviewing culture at pythonawesome which rivals have found impossible to imitate.

Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources

Black-box-Adversarial-Reprogramming

GitHub

John

Adversarial training and data augmentation for neural question-answering models

A lightweight module for Multi-Task Learning in pytorch