117 research outputs found
Sparse Coding on Stereo Video for Object Detection
Deep Convolutional Neural Networks (DCNN) require millions of labeled
training examples for image classification and object detection tasks, which
restrict these models to domains where such datasets are available. In this
paper, we explore the use of unsupervised sparse coding applied to stereo-video
data to help alleviate the need for large amounts of labeled data. We show that
replacing a typical supervised convolutional layer with an unsupervised
sparse-coding layer within a DCNN allows for better performance on a car
detection task when only a limited number of labeled training examples is
available. Furthermore, the network that incorporates sparse coding allows for
more consistent performance over varying initializations and ordering of
training examples when compared to a fully supervised DCNN. Finally, we compare
activations between the unsupervised sparse-coding layer and the supervised
convolutional layer, and show that the sparse representation exhibits an
encoding that is depth selective, whereas encodings from the convolutional
layer do not exhibit such selectivity. These result indicates promise for using
unsupervised sparse-coding approaches in real-world computer vision tasks in
domains with limited labeled training data
Visualizing classification of natural video sequences using sparse, hierarchical models of cortex.
Recent work on hierarchical models of visual cortex has reported state-of-the-art accuracy on whole-scene labeling using natural still imagery. This raises the question of whether the reported accuracy may be due to the sophisticated, non-biological back-end supervised classifiers typically used (support vector machines) and/or the limited number of images used in these experiments. In particular, is the model classifying features from the object or the background? Previous work (Landecker, Brumby, et al., COSYNE 2010) proposed tracing the spatial support of a classifier’s decision back through a hierarchical cortical model to determine which parts of the image contributed to the classification, compared to the positions of objects in the scene. In this way, we can go beyond standard measures of accuracy to provide tools for visualizing and analyzing high-level object classification. We now describe new work exploring the extension of these ideas to detection of objects in video sequences of natural scenes
Sampling binary sparse coding QUBO models using a spiking neuromorphic processor
We consider the problem of computing a sparse binary representation of an
image. To be precise, given an image and an overcomplete, non-orthonormal
basis, we aim to find a sparse binary vector indicating the minimal set of
basis vectors that when added together best reconstruct the given input. We
formulate this problem with an loss on the reconstruction error, and an
(or, equivalently, an ) loss on the binary vector enforcing
sparsity. This yields a so-called Quadratic Unconstrained Binary Optimization
(QUBO) problem, whose solution is generally NP-hard to find. The contribution
of this work is twofold. First, the method of unsupervised and unnormalized
dictionary feature learning for a desired sparsity level to best match the data
is presented. Second, the binary sparse coding problem is then solved on the
Loihi 1 neuromorphic chip by the use of stochastic networks of neurons to
traverse the non-convex energy landscape. The solutions are benchmarked against
the classical heuristic simulated annealing. We demonstrate neuromorphic
computing is suitable for sampling low energy solutions of binary sparse coding
QUBO models, and although Loihi 1 is capable of sampling very sparse solutions
of the QUBO models, there needs to be improvement in the implementation in
order to be competitive with simulated annealing
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