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Systems and Methods for Training Object Detection Models Using Adversarial Examples

机译:使用对抗示例训练物体检测模型的系统和方法

摘要

Systems and methods for training object detection models using adversarial examples are provided. A method includes obtaining a training scene and identifying a target object within the training scene. The method includes obtaining an adversarial object and generating a modified training scene based on the adversarial object, the target object, and the training scene. The modified training scene includes the training scene modified to include the adversarial object placed on the target object. The modified training scene is input to a machine-learned model configured to detect the training object. A detection score is determined based on whether the training object is detected, and the machine-learned model and the parameters of the adversarial object are trained based on the detection output. The machine-learned model is trained to maximize the detection output. The parameters of the adversarial object are trained to minimize the detection output.
机译:提供了使用对抗示例进行训练对象检测模型的系统和方法。 一种方法包括获得训练场景并识别训练场景中的目标对象。 该方法包括获得对抗性对象并基于对手对象,目标对象和训练场景生成修改的训练场景。 修改的训练场景包括修改的训练场景,以包括放置在目标对象上的对手对象。 修改后的训练场景被输入到配置以检测训练对象的机器学习模型。 基于检测训练对象是否检测到检测分数,并且基于检测输出培训机器学习模型和对手对象的参数。 培训机器学习模型以最大化检测输出。 培训对抗物体的参数,以最小化检测输出。

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