4,560 research outputs found
Semi-supervised FusedGAN for Conditional Image Generation
We present FusedGAN, a deep network for conditional image synthesis with
controllable sampling of diverse images. Fidelity, diversity and controllable
sampling are the main quality measures of a good image generation model. Most
existing models are insufficient in all three aspects. The FusedGAN can perform
controllable sampling of diverse images with very high fidelity. We argue that
controllability can be achieved by disentangling the generation process into
various stages. In contrast to stacked GANs, where multiple stages of GANs are
trained separately with full supervision of labeled intermediate images, the
FusedGAN has a single stage pipeline with a built-in stacking of GANs. Unlike
existing methods, which requires full supervision with paired conditions and
images, the FusedGAN can effectively leverage more abundant images without
corresponding conditions in training, to produce more diverse samples with high
fidelity. We achieve this by fusing two generators: one for unconditional image
generation, and the other for conditional image generation, where the two
partly share a common latent space thereby disentangling the generation. We
demonstrate the efficacy of the FusedGAN in fine grained image generation tasks
such as text-to-image, and attribute-to-face generation
Risk-return Efficiency, Financial Distress Risk, and Bank Financial Strength Ratings
This paper investigates whether there is any consistency between banks' financial strength ratings (bank rating) and their risk-return profiles. It is expected that banks with high ratings tend to earn high expected returns for the risks they assume and thereby have a low probability of experiencing financial distress. Bank ratings, a measure of a bank's intrinsic safety and soundness, should therefore be able to capture the bank's ability to manage financial distress while achieving risk-return efficiency. We first estimate the expected returns, risks, and financial distress risk proxy (the inverse z-score), then apply the stochastic frontier analysis (SFA) to obtain the risk-return efficiency score for each bank, and finally conduct ordered logit regressions of bank ratings on estimated risks, risk-return efficiency, and the inverse z-score by controlling for other variables related to each bank's operating environment. We find that banks with a higher efficiency score on average tend to obtain favorable ratings. It appears that rating agencies generally encourage banks to trade expected returns for reduced risks, suggesting that these ratings are generally consistent with banks' risk-return profiles.bank ratings; risk-return efficiency; stochastic frontier analysis
Collaborative Deep Reinforcement Learning for Joint Object Search
We examine the problem of joint top-down active search of multiple objects
under interaction, e.g., person riding a bicycle, cups held by the table, etc..
Such objects under interaction often can provide contextual cues to each other
to facilitate more efficient search. By treating each detector as an agent, we
present the first collaborative multi-agent deep reinforcement learning
algorithm to learn the optimal policy for joint active object localization,
which effectively exploits such beneficial contextual information. We learn
inter-agent communication through cross connections with gates between the
Q-networks, which is facilitated by a novel multi-agent deep Q-learning
algorithm with joint exploitation sampling. We verify our proposed method on
multiple object detection benchmarks. Not only does our model help to improve
the performance of state-of-the-art active localization models, it also reveals
interesting co-detection patterns that are intuitively interpretable
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio
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