14,611 research outputs found
Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting
Recent work has proposed various adversarial loss functions for training
either generative or discriminative models. Yet, it remains unclear what
certain types of functions are valid adversarial losses, and how these loss
functions perform against one another. In this paper, we aim to gain a deeper
understanding of adversarial losses by decoupling the effects of their
component functions and regularization terms. We first derive in theory some
necessary and sufficient conditions of the component functions such that the
adversarial loss is a divergence-like measure between the data and the model
distributions. In order to systematically compare different adversarial losses,
we then propose a new, simple comparative framework, dubbed DANTest, based on
discriminative adversarial networks (DANs). With this framework, we evaluate an
extensive set of adversarial losses by combining different component functions
and regularization approaches. Our theoretical and empirical results can
together serve as a reference for choosing or designing adversarial training
objectives in future research
Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation
Automatic parsing of anatomical objects in X-ray images is critical to many
clinical applications in particular towards image-guided invention and workflow
automation. Existing deep network models require a large amount of labeled
data. However, obtaining accurate pixel-wise labeling in X-ray images relies
heavily on skilled clinicians due to the large overlaps of anatomy and the
complex texture patterns. On the other hand, organs in 3D CT scans preserve
clearer structures as well as sharper boundaries and thus can be easily
delineated. In this paper, we propose a novel model framework for learning
automatic X-ray image parsing from labeled CT scans. Specifically, a Dense
Image-to-Image network (DI2I) for multi-organ segmentation is first trained on
X-ray like Digitally Reconstructed Radiographs (DRRs) rendered from 3D CT
volumes. Then we introduce a Task Driven Generative Adversarial Network
(TD-GAN) architecture to achieve simultaneous style transfer and parsing for
unseen real X-ray images. TD-GAN consists of a modified cycle-GAN substructure
for pixel-to-pixel translation between DRRs and X-ray images and an added
module leveraging the pre-trained DI2I to enforce segmentation consistency. The
TD-GAN framework is general and can be easily adapted to other learning tasks.
In the numerical experiments, we validate the proposed model on 815 DRRs and
153 topograms. While the vanilla DI2I without any adaptation fails completely
on segmenting the topograms, the proposed model does not require any topogram
labels and is able to provide a promising average dice of 85% which achieves
the same level accuracy of supervised training (88%)
Towards Better Interpretability in Deep Q-Networks
Deep reinforcement learning techniques have demonstrated superior performance
in a wide variety of environments. As improvements in training algorithms
continue at a brisk pace, theoretical or empirical studies on understanding
what these networks seem to learn, are far behind. In this paper we propose an
interpretable neural network architecture for Q-learning which provides a
global explanation of the model's behavior using key-value memories, attention
and reconstructible embeddings. With a directed exploration strategy, our model
can reach training rewards comparable to the state-of-the-art deep Q-learning
models. However, results suggest that the features extracted by the neural
network are extremely shallow and subsequent testing using out-of-sample
examples shows that the agent can easily overfit to trajectories seen during
training.Comment: Accepted at AAAI-19; (16 pages, 18 figures
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
In this paper, we investigate deep image synthesis guided by sketch, color,
and texture. Previous image synthesis methods can be controlled by sketch and
color strokes but we are the first to examine texture control. We allow a user
to place a texture patch on a sketch at arbitrary locations and scales to
control the desired output texture. Our generative network learns to synthesize
objects consistent with these texture suggestions. To achieve this, we develop
a local texture loss in addition to adversarial and content loss to train the
generative network. We conduct experiments using sketches generated from real
images and textures sampled from a separate texture database and results show
that our proposed algorithm is able to generate plausible images that are
faithful to user controls. Ablation studies show that our proposed pipeline can
generate more realistic images than adapting existing methods directly.Comment: CVPR 2018 spotligh
Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by
considering that a network is trained on multiple tasks, but performs one task
at a time, an approach we refer to as "single-tasking multiple tasks". The
network thus modifies its behaviour through task-dependent feature adaptation,
or task attention. This gives the network the ability to accentuate the
features that are adapted to a task, while shunning irrelevant ones. We further
reduce task interference by forcing the task gradients to be statistically
indistinguishable through adversarial training, ensuring that the common
backbone architecture serving all tasks is not dominated by any of the
task-specific gradients. Results in three multi-task dense labelling problems
consistently show: (i) a large reduction in the number of parameters while
preserving, or even improving performance and (ii) a smooth trade-off between
computation and multi-task accuracy. We provide our system's code and
pre-trained models at http://vision.ee.ethz.ch/~kmaninis/astmt/.Comment: CVPR 2019 Camera Read
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