Deep learning applications usually involve complex optimisation problems that are often difficult to solve analytically.
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Often the objective function itself may not be in analytically closed-form, which means that the objective function only permits function evaluations without any gradient evaluations. This is where Zeroth-Order comes in.
Optimisation corresponding to the above types of problems falls into the category of Zeroth-Order (ZO) optimisation with respect to the black-box models , where explicit expressions of the gradients are hard to estimate or infeasible to obtain.
Researchers from IBM Research and MIT-IBM Watson Lab discussed the topic of Zeroth-Order optimisation at the on-going Computer Vision and Pattern Recognition (CVPR) 2020 conference. In this article, we will take a dive into what Zeroth-Order optimisation is and how this method can be applied in complex applications.
Behind ZO Optimisation
Zeroth-Order (ZO) optimisation is a subset of gradient-free optimisation that emerges in various signal processing as well as applications. ZO optimisation methods are basically the gradient-free counterparts of first-order (FO) optimisation techniques. ZO approximates the full gradients or stochastic gradients through function value-based gradient estimates.
Derivative-Free methods for black-box optimisation has been studied by the optimisation community for many years now. However, conventional Derivative-Free optimisation methods have two main shortcomings that include difficulties to scale to large-size problems and lack of convergence rate analysis.
ZO optimisation has the following three main advantages over the Derivative-Free optimisation methods:
Applications Of ZO Optimisation
ZO optimisation has drawn increasing attention due to its success in solving emerging signal processing and
According to Pin-Yu Chen, a researcher at IBM Research, Zeroth-order (ZO) optimisation achieves gradient-free optimisation by approximating the full gradient via efficient gradient estimators.
Some recent important applications include generation of prediction-evasive, black-box adversarial attacks on deep neural networks, generation of model-agnostic explanation from
ZO Optimisation For Adversarial Robustness In Deep Learning
Talking about the application of ZO optimisation to the generation of prediction-evasive adversarial examples to fool
In most of the cases, the internal states or configurations and the operating mechanism of
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