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Auflistung nach Schlagwort "Curriculum learning"

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  • Konferenzbeitrag
    On Brightness Agnostic Adversarial Examples Against Face Recognition Systems
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Singh, Inderjeet; Momiyama, Satoru; Kakizaki, Kazuya; Araki, Toshinori
    This paper introduces a novel adversarial example generation method against face recognition systems (FRSs). An adversarial example (AX) is an image with deliberately crafted noise to cause incorrect predictions by a target system. The AXs generated from our method remain robust under real-world brightness changes. Our method performs non-linear brightness transformations while leveraging the concept of curriculum learning during the attack generation procedure. We demonstrate that our method outperforms conventional techniques from comprehensive experimental investigations in the digital and physical world. Furthermore, this method enables practical risk assessment of FRSs against brightness agnostic AXs.
  • Zeitschriftenartikel
    Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning
    (KI - Künstliche Intelligenz: Vol. 35, No. 1, 2021) Nguyen, Sao Mai; Duminy, Nicolas; Manoury, Alexandre; Duhaut, Dominique; Buche, Cedric
    Multi-task learning by robots poses the challenge of the domain knowledge: complexity of tasks, complexity of the actions required, relationship between tasks for transfer learning. We demonstrate that this domain knowledge can be learned to address the challenges in life-long learning. Specifically, the hierarchy between tasks of various complexities is key to infer a curriculum from simple to composite tasks. We propose a framework for robots to learn sequences of actions of unbounded complexity in order to achieve multiple control tasks of various complexity. Our hierarchical reinforcement learning framework, named SGIM-SAHT, offers a new direction of research, and tries to unify partial implementations on robot arms and mobile robots. We outline our contributions to enable robots to map multiple control tasks to sequences of actions: representations of task dependencies, an intrinsically motivated exploration to learn task hierarchies, and active imitation learning. While learning the hierarchy of tasks, it infers its curriculum by deciding which tasks to explore first, how to transfer knowledge, and when, how and whom to imitate.