TASK DEFINITION: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
PROBLEM: In deep learning, performance is strongly affected by the choice of architecture
 and hyperparameters. While there has been extensive work on automatic hyperpa-
 rameter optimization for simple spaces, complex spaces such as the space of deep
 architectures remain largely unexplored. As a result, the choice of architecture is
 done manually by the human expert through a slow trial and error process guided
 mainly by intuition. In this paper we describe a framework for automatically
 designing and training deep models. We propose an extensible and modular lan-
 guage that allows the human expert to compactly represent complex search spaces
 over architectures and their hyperparameters. The resulting search spaces are tree-
 structured and therefore easy to traverse. Models can be automatically compiled to
 computational graphs once values for all hyperparameters have been chosen. We
 can leverage the structure of the search space to introduce different model search
 algorithms, such as random search, Monte Carlo tree search (MCTS), and sequen-
 tial model-based optimization (SMBO). We present experiments comparing the
 different algorithms on CIFAR-10 and show that MCTS and SMBO outperform
 random search. We also present experiments on MNIST, showing that the same
 search space achieves near state-of-the-art performance with a few samples. These
 experiments show that our framework can be used effectively for model discov-
 ery, as it is possible to describe expressive search spaces and discover competitive
 models without much effort from the human expert. Code for our framework and
 experiments has been made publicly available

SOLUTION: We describe a modular and composable language for describing expressive search spaces over architectures and simple model search algorithms applied to these search spaces. 

PROBLEM: Deep reinforcement learning (DRL) algorithms have demonstrated progress in learning to find a goal in challenging environments. As the title of the paper by Mirowski et al. (2016) suggests, one might assume that DRL-based algorithms are able to “learn to navigate” and are thus ready to replace classical mapping and path-planning algorithms, at least in simulated environments. Yet, from experiments and analysis in this earlier work, it is not clear what strategies are used by these algorithms in navigating the mazes and finding the goal. In this paper, we pose and study this underlying question: are DRL algorithms doing some form of mapping and/or path-planning? Our experiments show that the algorithms are not memorizing the maps of mazes at the testing stage but, rather, at the training stage. Hence, the DRL algorithms fall short of qualifying as mapping or path-planning algorithms with any reasonable definition of mapping. We extend the experiments in Mirowski et al. (2016) by separating the set of training and testing maps and by a more ablative coverage of the space of experiments. Our systematic experiments show that the NavA3C-D1-D2-L algorithm, when trained and tested on the same maps, is able to choose the shorter paths to the goal. However, when tested on unseen maps the algorithm utilizes a wall-following strategy to find the goal without doing any mapping or path planning.

SOLUTION: We quantitatively and qualitatively evaluate deep reinforcement learning based navigation methods under a variety of conditions to answer the question of how close they are to replacing classical path planners and mapping algorithms.

PROBLEM: Deep Learning for Computer Vision depends mainly on the source of supervision. Photo-realistic simulators can generate large-scale automatically labeled synthetic data, but introduce a domain gap negatively impacting performance. We propose a new unsupervised domain adaptation algorithm, called SPIGAN, relying on Simulator Privileged Information (PI) and Generative Adversarial Networks (GAN). We use internal data from the simulator as PI during the training of a target task network. We experimentally evaluate our approach on semantic segmentation. We train the networks on real-world Cityscapes and Vistas datasets, using only unlabeled real-world images and synthetic labeled data with z-buffer (depth) PI from the SYNTHIA dataset. Our method improves over no adaptation and state-of-the-art unsupervised domain adaptation techniques.

SOLUTION:
An unsupervised sim-to-real domain adaptation method for semantic segmentation using privileged information from a simulator with GAN-based image translation.