109 research outputs found
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Traditional architectures for solving computer vision problems and the degree
of success they enjoyed have been heavily reliant on hand-crafted features.
However, of late, deep learning techniques have offered a compelling
alternative -- that of automatically learning problem-specific features. With
this new paradigm, every problem in computer vision is now being re-examined
from a deep learning perspective. Therefore, it has become important to
understand what kind of deep networks are suitable for a given problem.
Although general surveys of this fast-moving paradigm (i.e. deep-networks)
exist, a survey specific to computer vision is missing. We specifically
consider one form of deep networks widely used in computer vision -
convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN
and then examine the broad variations proposed over time to suit different
applications. We hope that our recipe-style survey will serve as a guide,
particularly for novice practitioners intending to use deep-learning techniques
for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm
Collaborative Layer-wise Discriminative Learning in Deep Neural Networks
Intermediate features at different layers of a deep neural network are known
to be discriminative for visual patterns of different complexities. However,
most existing works ignore such cross-layer heterogeneities when classifying
samples of different complexities. For example, if a training sample has
already been correctly classified at a specific layer with high confidence, we
argue that it is unnecessary to enforce rest layers to classify this sample
correctly and a better strategy is to encourage those layers to focus on other
samples.
In this paper, we propose a layer-wise discriminative learning method to
enhance the discriminative capability of a deep network by allowing its layers
to work collaboratively for classification. Towards this target, we introduce
multiple classifiers on top of multiple layers. Each classifier not only tries
to correctly classify the features from its input layer, but also coordinates
with other classifiers to jointly maximize the final classification
performance. Guided by the other companion classifiers, each classifier learns
to concentrate on certain training examples and boosts the overall performance.
Allowing for end-to-end training, our method can be conveniently embedded into
state-of-the-art deep networks. Experiments with multiple popular deep
networks, including Network in Network, GoogLeNet and VGGNet, on scale-various
object classification benchmarks, including CIFAR100, MNIST and ImageNet, and
scene classification benchmarks, including MIT67, SUN397 and Places205,
demonstrate the effectiveness of our method. In addition, we also analyze the
relationship between the proposed method and classical conditional random
fields models.Comment: To appear in ECCV 2016. Maybe subject to minor changes before
camera-ready versio
New architectures for very deep learning
Artificial Neural Networks are increasingly being used in complex real- world applications because many-layered (i.e., deep) architectures can now be trained on large quantities of data. However, training even deeper, and therefore more powerful networks, has hit a barrier due to fundamental limitations in the design of existing networks. This thesis develops new architectures that, for the first time, allow very deep networks to be optimized efficiently and reliably. Specifically, it addresses two key issues that hamper credit assignment in neural networks: cross-pattern interference and vanishing gradients. Cross- pattern interference leads to oscillations of the network’s weights that make training inefficient. The proposed Local Winner-Take-All networks reduce interference among computation units in the same layer through local competition. An in-depth analysis of locally competitive networks provides generalizable insights and reveals unifying properties that improve credit assignment. As network depth increases, vanishing gradients make a network’s outputs increasingly insensitive to the weights close to the inputs, causing the failure of gradient-based training. To overcome this limitation, the proposed Highway networks regulate information flow across layers through additional skip connections which are modulated by learned computation units. Their beneficial properties are extended to the sequential domain with Recurrent Highway Networks that gain from increased depth and learn complex sequential transitions without requiring more parameters
- …