2,023 research outputs found
Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
When working with three-dimensional data, choice of representation is key. We
explore voxel-based models, and present evidence for the viability of
voxellated representations in applications including shape modeling and object
classification. Our key contributions are methods for training voxel-based
variational autoencoders, a user interface for exploring the latent space
learned by the autoencoder, and a deep convolutional neural network
architecture for object classification. We address challenges unique to
voxel-based representations, and empirically evaluate our models on the
ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the
state of the art for object classification.Comment: 9 pages, 5 figures, 2 table
Generative Adversarial Text to Image Synthesis
Automatic synthesis of realistic images from text would be interesting and
useful, but current AI systems are still far from this goal. However, in recent
years generic and powerful recurrent neural network architectures have been
developed to learn discriminative text feature representations. Meanwhile, deep
convolutional generative adversarial networks (GANs) have begun to generate
highly compelling images of specific categories, such as faces, album covers,
and room interiors. In this work, we develop a novel deep architecture and GAN
formulation to effectively bridge these advances in text and image model- ing,
translating visual concepts from characters to pixels. We demonstrate the
capability of our model to generate plausible images of birds and flowers from
detailed text descriptions.Comment: ICML 201
What can linear interpolation of neural network loss landscapes tell us?
Studying neural network loss landscapes provides insights into the nature of
the underlying optimization problems. Unfortunately, loss landscapes are
notoriously difficult to visualize in a human-comprehensible fashion. One
common way to address this problem is to plot linear slices of the landscape,
for example from the initial state of the network to the final state after
optimization. On the basis of this analysis, prior work has drawn broader
conclusions about the difficulty of the optimization problem. In this paper, we
put inferences of this kind to the test, systematically evaluating how linear
interpolation and final performance vary when altering the data, choice of
initialization, and other optimizer and architecture design choices. Further,
we use linear interpolation to study the role played by individual layers and
substructures of the network. We find that certain layers are more sensitive to
the choice of initialization and optimizer hyperparameter settings, and we
exploit these observations to design custom optimization schemes. However, our
results cast doubt on the broader intuition that the presence or absence of
barriers when interpolating necessarily relates to the success of optimization
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