3,122 research outputs found
Deep supervised learning using local errors
Error backpropagation is a highly effective mechanism for learning
high-quality hierarchical features in deep networks. Updating the features or
weights in one layer, however, requires waiting for the propagation of error
signals from higher layers. Learning using delayed and non-local errors makes
it hard to reconcile backpropagation with the learning mechanisms observed in
biological neural networks as it requires the neurons to maintain a memory of
the input long enough until the higher-layer errors arrive. In this paper, we
propose an alternative learning mechanism where errors are generated locally in
each layer using fixed, random auxiliary classifiers. Lower layers could thus
be trained independently of higher layers and training could either proceed
layer by layer, or simultaneously in all layers using local error information.
We address biological plausibility concerns such as weight symmetry
requirements and show that the proposed learning mechanism based on fixed,
broad, and random tuning of each neuron to the classification categories
outperforms the biologically-motivated feedback alignment learning technique on
the MNIST, CIFAR10, and SVHN datasets, approaching the performance of standard
backpropagation. Our approach highlights a potential biological mechanism for
the supervised, or task-dependent, learning of feature hierarchies. In
addition, we show that it is well suited for learning deep networks in custom
hardware where it can drastically reduce memory traffic and data communication
overheads
Object Level Deep Feature Pooling for Compact Image Representation
Convolutional Neural Network (CNN) features have been successfully employed
in recent works as an image descriptor for various vision tasks. But the
inability of the deep CNN features to exhibit invariance to geometric
transformations and object compositions poses a great challenge for image
search. In this work, we demonstrate the effectiveness of the objectness prior
over the deep CNN features of image regions for obtaining an invariant image
representation. The proposed approach represents the image as a vector of
pooled CNN features describing the underlying objects. This representation
provides robustness to spatial layout of the objects in the scene and achieves
invariance to general geometric transformations, such as translation, rotation
and scaling. The proposed approach also leads to a compact representation of
the scene, making each image occupy a smaller memory footprint. Experiments
show that the proposed representation achieves state of the art retrieval
results on a set of challenging benchmark image datasets, while maintaining a
compact representation.Comment: Deep Vision 201
Retinal vessel segmentation using Gabor Filter and Textons
This paper presents a retinal vessel segmentation method that is inspired by the human visual system and uses a Gabor filter bank. Machine learning is used to optimize the filter parameters for retinal vessel extraction. The filter responses are represented as textons and this allows the corresponding membership functions to be used as the framework for learning vessel and non-vessel classes. Then, vessel texton memberships are used to generate segmentation results. We evaluate our method using the publicly available DRIVE database. It achieves competitive performance (sensitivity=0.7673, specificity=0.9602, accuracy=0.9430) compared to other recently published work. These figures are particularly interesting as our filter bank is quite generic and only includes Gabor responses. Our experimental results also show that the performance, in terms of sensitivity, is superior to other methods
SpotNet - Learned iterations for cell detection in image-based immunoassays
Accurate cell detection and counting in the image-based ELISpot and
FluoroSpot immunoassays is a challenging task. Recently proposed methodology
matches human accuracy by leveraging knowledge of the underlying physical
process of these assays and using proximal optimization methods to solve an
inverse problem. Nonetheless, thousands of computationally expensive iterations
are often needed to reach a near-optimal solution. In this paper, we exploit
the structure of the iterations to design a parameterized computation graph,
SpotNet, that learns the patterns embedded within several training images and
their respective cell information. Further, we compare SpotNet to a
convolutional neural network layout customized for cell detection. We show
empirical evidence that, while both designs obtain a detection performance on
synthetic data far beyond that of a human expert, SpotNet is easier to train
and obtains better estimates of particle secretion for each cell.Comment: 5 pages, 4 figures, 2019 IEEE 16th International Symposium on
Biomedical Imaging (ISBI 2019), Venice, Italy, April 8-11, 201
A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits
The recognition of handwritten digits is an application which
has been used as a benchmark for comparing shape recognition methods.
We train COSFIRE filters to be selective for different parts of handwritten
digits. In analogy with the neurophysiological concept of population
coding we use the responses of multiple COSFIRE filters as a shape descriptor
of a handwritten digit. We demonstrate the effectiveness of the
proposed approach on two data sets of handwritten digits: Western Arabic
(MNIST) and Farsi for which we achieve high recognition rates of
99.52% and 99.33%, respectively. COSFIRE filters are conceptually simple,
easy to implement and they are versatile trainable feature detectors.
The shape descriptor that we propose is highly effective to the automatic
recognition of handwritten digits.peer-reviewe
Digging into acceptor splice site prediction : an iterative feature selection approach
Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and feature construction steps, improving the classification of acceptor splice sites, an important subtask of gene prediction.
We show that acceptor prediction can benefit from feature selection, and describe how feature selection techniques can be used to gain new insights in the classification of acceptor sites. This is illustrated by the identification of a new, biologically motivated feature: the AG-scanning feature.
The results described in this paper contribute both to the domain of gene prediction, and to research in feature selection techniques, describing a new wrapper based feature weighting method that aids in knowledge discovery when dealing with complex datasets
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