Abstract-Fast track is a software speculation system that enables unsafe optimization of sequential code. It speculatively runs optimized code to improve performance and then checks the correctness of the speculative code by running the original program on multiple processors.We present the interface design and system implementation for Fast Track. It lets a programmer or a profiling tool mark fast-track code regions and uses a run-time system to manage the parallel execution of the speculative process and its checking processes and ensures the correct display of program outputs. The core of the run-time system is a novel concurrent algorithm that balances exploitable parallelism and available processors when the fast track is too slow or too fast. The programming interface closely affects the run-time support. Our system permits both explicit and implicit end markers for speculatively optimized code regions as well as extensions that allow the use of multiple tracks and user defined correctness checking. We discuss the possible uses of speculative optimization and demonstrate the effectiveness of our prototype system by examples of unsafe semantic optimization and a general system for fast memory-safety checking, which is able to reduce the checking time by factors between 2 and 7 for large sequential code on a 8-CPU system.
Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples.
Unlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically.
During the past few years, adversarial training has been studied and discussed from various aspects, which deserves a comprehensive review.
For the first time in this survey, we systematically review the recent progress on adversarial training for adversarial robustness with a novel taxonomy.
Then we discuss the generalization problems in adversarial training from three perspectives and highlight the challenges which are not fully tackled.
Finally, we present potential future directions.
Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models. Unlike other defense strategies, adversarial training aims to promote the robustness of models intrinsically. During the last few years, adversarial training has been studied and discussed from various aspects. A variety of improvements and developments of adversarial training are proposed, which were, however, neglected in existing surveys. For the first time in this survey, we systematically review the recent progress on adversarial training for adversarial robustness with a novel taxonomy. Then we discuss the generalization problems in adversarial training from three perspectives. Finally, we highlight the challenges which are not fully tackled and present potential future directions.
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