4,751 research outputs found
Understanding Deep Architectures using a Recursive Convolutional Network
A key challenge in designing convolutional network models is sizing them
appropriately. Many factors are involved in these decisions, including number
of layers, feature maps, kernel sizes, etc. Complicating this further is the
fact that each of these influence not only the numbers and dimensions of the
activation units, but also the total number of parameters. In this paper we
focus on assessing the independent contributions of three of these linked
variables: The numbers of layers, feature maps, and parameters. To accomplish
this, we employ a recursive convolutional network whose weights are tied
between layers; this allows us to vary each of the three factors in a
controlled setting. We find that while increasing the numbers of layers and
parameters each have clear benefit, the number of feature maps (and hence
dimensionality of the representation) appears ancillary, and finds most of its
benefit through the introduction of more weights. Our results (i) empirically
confirm the notion that adding layers alone increases computational power,
within the context of convolutional layers, and (ii) suggest that precise
sizing of convolutional feature map dimensions is itself of little concern;
more attention should be paid to the number of parameters in these layers
instead
A Meta-Learning Approach for Custom Model Training
Transfer-learning and meta-learning are two effective methods to apply
knowledge learned from large data sources to new tasks. In few-class, few-shot
target task settings (i.e. when there are only a few classes and training
examples available in the target task), meta-learning approaches that optimize
for future task learning have outperformed the typical transfer approach of
initializing model weights from a pre-trained starting point. But as we
experimentally show, meta-learning algorithms that work well in the few-class
setting do not generalize well in many-shot and many-class cases. In this
paper, we propose a joint training approach that combines both
transfer-learning and meta-learning. Benefiting from the advantages of each,
our method obtains improved generalization performance on unseen target tasks
in both few- and many-class and few- and many-shot scenarios.Comment: AAAI 201
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
In this paper we address three different computer vision tasks using a single
basic architecture: depth prediction, surface normal estimation, and semantic
labeling. We use a multiscale convolutional network that is able to adapt
easily to each task using only small modifications, regressing from the input
image to the output map directly. Our method progressively refines predictions
using a sequence of scales, and captures many image details without any
superpixels or low-level segmentation. We achieve state-of-the-art performance
on benchmarks for all three tasks
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
We present an integrated framework for using Convolutional Networks for
classification, localization and detection. We show how a multiscale and
sliding window approach can be efficiently implemented within a ConvNet. We
also introduce a novel deep learning approach to localization by learning to
predict object boundaries. Bounding boxes are then accumulated rather than
suppressed in order to increase detection confidence. We show that different
tasks can be learned simultaneously using a single shared network. This
integrated framework is the winner of the localization task of the ImageNet
Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very
competitive results for the detection and classifications tasks. In
post-competition work, we establish a new state of the art for the detection
task. Finally, we release a feature extractor from our best model called
OverFeat
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