Face grouping remains a challenging problem despite the remarkable capability of deep learning approaches in learning face representation. In particular, grouping results can still be egregious given profile faces and a large number of uninteresting faces and noisy detections. Often, a user needs to correct the erroneous grouping manually. In this study, we formulate a novel face grouping framework that learns clustering strategy from ground-truth simulated behavior. This is achieved through imitation learning (a.k.a apprenticeship learning or learning by watching) via inverse reinforcement learning (IRL). In contrast to existing clustering approaches that group instances by similarity, our framework makes sequential decision to dynamically decide when to merge two face instances/groups driven by short- and long-term rewards. Extensive experiments on three benchmark datasets show that our framework outperforms unsupervised and supervised baselines.
Building on the founders' pioneering research in deep imitation learning, deep reinforcement learning and meta-learning, Embodied Intelligence is developing AI software (aka robot brains) that can be loaded onto any existing robots. While traditional programming of robots requires writing code, a time-consuming endeavor even for robotics experts, Embodied Intelligence software will empower anyone to program a robot by simply donning a VR headset and guiding a robot through a task. These human demonstrations train deep neural nets, which are further tuned through the use of reinforcement learning, resulting in robots that can be easily taught a wide range of skills in areas where existing solutions break down. Complicated tasks like the manipulation of deformable objects such as wires, fabrics, linens, apparel, fluid-bags, and food; picking parts and order items out of cluttered, unstructured bins; completing assemblies where hard automation struggles due to variability in parts, configurations, and individualization of orders, are all candidates to benefit from Embodied Intelligence's work.
Automatic analysis of game levels can provide as- sistance to game designers and procedural content generation. We introduce a static-dynamic scale to categorize level analysis strategies, which captures the extent that the analysis depends on player simulation. Due to its ability to automatically learn intermediate representations for the task, a convolutional neural network (CNN) provides a general tool for both types of analysis. In this paper, we explore the use of CNN to analyze 1,437 Infinite Mario levels. We further propose a deep reinforcement learning technique for dynamic analysis, which allows the simulated player to pay a penalty to reduce error in its control. We empirically demonstrate the effectiveness of our techniques and complementarity of dynamic and static analysis.
Now, if there is something that data scientists like to do, is merge concepts and create new beautiful and unexpected models. That is why in this article, we will find out what happens when we give the learning agent ability to "see", i.e. what happens when we involve convolutional neural networks into Deep Q-Learning framework.