174 research outputs found
CIFAR-10: KNN-based Ensemble of Classifiers
In this paper, we study the performance of different classifiers on the
CIFAR-10 dataset, and build an ensemble of classifiers to reach a better
performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and
Convolutional Neural Network (CNN), on some classes, are mutually exclusive,
thus yield in higher accuracy when combined. We reduce KNN overfitting using
Principal Component Analysis (PCA), and ensemble it with a CNN to increase its
accuracy. Our approach improves our best CNN model from 93.33% to 94.03%
Input Fast-Forwarding for Better Deep Learning
This paper introduces a new architectural framework, known as input
fast-forwarding, that can enhance the performance of deep networks. The main
idea is to incorporate a parallel path that sends representations of input
values forward to deeper network layers. This scheme is substantially different
from "deep supervision" in which the loss layer is re-introduced to earlier
layers. The parallel path provided by fast-forwarding enhances the training
process in two ways. First, it enables the individual layers to combine
higher-level information (from the standard processing path) with lower-level
information (from the fast-forward path). Second, this new architecture reduces
the problem of vanishing gradients substantially because the fast-forwarding
path provides a shorter route for gradient backpropagation. In order to
evaluate the utility of the proposed technique, a Fast-Forward Network (FFNet),
with 20 convolutional layers along with parallel fast-forward paths, has been
created and tested. The paper presents empirical results that demonstrate
improved learning capacity of FFNet due to fast-forwarding, as compared to
GoogLeNet (with deep supervision) and CaffeNet, which are 4x and 18x larger in
size, respectively. All of the source code and deep learning models described
in this paper will be made available to the entire research communityComment: Accepted in the 14th International Conference on Image Analysis and
Recognition (ICIAR) 2017, Montreal, Canad
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