Figure 8. The architecture of LeNet-5 convolutionalneural network for digit recogni- tion (LeCun et al., 1998).
Most convolutionalneuralnetworks use also a pooling layer, which makes the representation more invariant to smaller translations of the input. Meaning that small changes in input leave most of the pooled outputs unchanged. Pooling takes surrounding pixels into account, whether taking maximum pixel intensity, an average of pixels or some other aggregation of nearby pixels. For example, the max pooling (Zhou and Chellappa, 1988) returns maximum output from a rectangular area. The pooling is useful because it allows the inputs to vary a little, meaning that the feature of input image does not have to be in the exact same spot. For example, in case of face recognition, the position of eyes does not have to be always the same, however, it is necessary that there are 2 eyes, one on the left and another on the right side of the face. Additionally, pooling can be used to downsample inputs, as it summarizes the pixels over nearby images. In order to achieve a downsampling with a stride of 2, the maximum pixel intensity is returned for every other pixel.
Department of Computer Science and Engineering, Institute of Technology, Nirma University (India)
Neuralnetworks are one of the most powerful technologies that are used for a variety of classification and prediction problems. This paper summarizes convolutionalneural network which is the new buzzword in the world of machine learning and deep learning. They are similar to simple neuralnetworks. Convolutionalneuralnetworks involve a huge number of neurons. Each neuron has weights and biases associated with them which can be learned over time to fit the data properly. Convolutionalneuralnetworks, referred to as CNNs, are used in a variety of deep learning problems. They can be used for classification as well as prediction problems which involve images as input. Some of the examples of such problems are – facial key point detection, emotion detection, facial recognition, speech recognition, etc. In this paper, we will also focus on how much better are CNNs than simple neuralnetworks by illustrating our claims on MNIST data set.
The proposed method performs better when compared with Tompson et al.  when the normalized distances are large and performs worst when the same distances are small. New hybrid architecture  called as the Spatial Model that combines a deep Convolutionalneural network and a Markov random field is successfully applied to the long-standing task of human pose estimation. Spatial- Model does not have much impact on the accuracy of low radii threshold, however for large radii, the performance increases by around 8-12%. Thus, the unification of a novel CNN Part-Detector coupled with MRF inspired Spatial Model into a single learning framework outperforms all the existing architectures. State-of-the-art performance for human pose estimation has been achieved using Deep ConvolutionalNeuralNetworks. These networks use two approaches:
Typically convolutional layers are interspersed with sub-sampling layers to reduce computation time and to gradually build up further spatial and configural invariance. A small sub-sampling factor is desirable however in order to maintain specificity at the same time. Of course, this idea is not new, but the concept is both simple and powerful. The mammalian visual cortex and models thereof [12, 8, 7] draw heavily on these themes, and auditory neuroscience has revealed in the past ten years or so that these same design paradigms can be found in the primary and belt auditory areas of the cortex in a number of different animals [6, 11, 9]. Hierarchical analysis and learning architectures may yet be the key to success in the auditory domain.
Convolutionalneuralnetworks (CNNs)are one of the most widely used type of deep artificial neuralnetworks that are used in various fields such as image and video recognition, speech processing as well as natural language processing.
These networks have been inspired by biological processes, such as working of the visual cortex in cats and spider monkeys. Hubel and Wiesel, in the year 1969, studied the same to classify the cells in the cortex of these as- simple, complex and hypercomplex. The complex cells were found to have a receptive field, i.e., the area of response to stimulus, approximately twice as large as that of a simple cell. Hence the idea of using translational invariance in order to recognize visual imagery was realized.This property stated that the exact location of an object in an image was of less importance rather than detecting the object. Using convolutionalneuralnetworks is better than fully connected networks in many applications since instead of every node in a layer being connected to every other node in the previous layer, the former has every node in the m’th layer being connected to n nodes in the (m-1)’th layer, where n is the size of the receptive field of the CNN. This reduces the total number of parameters in the network and hence prevents overfitting and also ensures a built-in invariance. The first convolutional network was introduced by LeCun et al, in 1998, known as Lenet-5.It was a 7 layered network which was used to digitize hand written digits on checks in banks. It introduced 3 basic architectures used in the network - shared weights, local receptive fields and pooling.
Abstract: A new pansharpening method is proposed, based on convolutionalneuralnetworks.
We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.
In the present work we have described a series of experiments with convolutionalneuralnetworks built on top of word2vec. Despite little tuning of hyperparameters, a simple CNN with one layer of convolution performs remarkably well. Our re- sults add to the well-established evidence that un- supervised pre-training of word vectors is an im- portant ingredient in deep learning for NLP.
The models’ size of ConvolutionalNeuralNetworks (CNNs) is usually too large to be deployed on the mobile devices and they often suffer from the over-fitting problem caused by the less abundant datasets. As illustrated in (Wu et al. 2018), most of the learned parameters are close to zero and the ac- tivated feature maps from different channels in a layer share similar geometric characteristics. This implies the parame- ters of the convolutional kernel are very redundant. There- fore, numerous methods, which can be classified into two categories, have been proposed to compress the networks. The algorithms in the first category are mostly based on pruning the weights or neurons and quantizing the weights Copyright c 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
2.1 Artificial NeuralNetworks
Artificial NeuralNetworks are a family of models inspired by the way biolog- ical nervous systems, such as the brain, process information which enables a computer to learn from data. There are different types of NNs but in this thesis are only presented two of them: (1) feed-forward networks and (2) convolutionalneuralnetworks. First it is introduced the architecture of a feed-forward network. Thus, this type of nets are composed by at least three layers, one input layer, one output layer and one or more hidden layers. A feed-forward network has the following characteristics:
1 ConvolutionalNeuralNetworks Architectures
We will survey the some most famous convolutionalneural net architectures.
LeNet. Among the earlier CNN architectures, LeNet is the most widely known. LeNet was used mostly for handwritten digit recognition on the MNIST dataset. Importantly, LeNet used a series of convolutional layers, then pooling layers, followed by several fully connected (FC) layers.
Labeling Paths with ConvolutionalNeuralNetworks
Kyle Wuerch and Sean Wallace
Abstract— With the increasing development of autonomous vehicles, being able to detect driveable paths in arbitrary environments has become a prevalent problem in multiple industries. This project explores a technique which utilizes a discretized output map that is used to color an image based on the confidence that each block is a driveable path. This was done using a generalized convolutionalneural network that was trained on a set of 3000 images taken from the perspective of a robot along with matching masks marking which portion of the image was a driveable path. The techniques used allowed for a labeling accuracy of over 95%.
With the increased accessibility to powerful GPUs, ability to develop machine learning algorithms has increased significantly. Coupled with open source deep learning frameworks, average users are now able to experiment with convolutionalneuralnetworks (CNNs) to solve novel problems. This project sought to create a CNN capable of classifying between various locations within a building. A single continuous video was taken while standing at each desired location so that every class in the neural network was represented by a single video. Each location was given a number to be used for classification and the video was subsequently titled locX, see Figure 2 for mapping to Building 14. These videos were converted to frames to train several well known CNNs using fine-tuning. Once the CNN was trained, it was verified against a set of test photos taken separately from the original training videos.
Struc and Peter Peer . The project addressees the identification of people by the biometry of their earlobe. In this thesis the detection of ears in images will be covered. This will help building a database of earlobes as well as detecting ears in an image in the process of human identification. Moreover a database of 1,000 annotated images will be built for the purpose of solving earlobe segmentation problems. The problem of earlobe detection will be solved using ConvolutionalNeuralNetworks (CNN) since it has (as far as we know) not yet been solved using this method. Annotation of earlobes for the database will be done manually. Binary masks will be created, where the marked areas will present an earlobe and the rest background. The images will be properly divided in the train and the test set and the created database will be used for training of the CNN as well as testing to obtain information about the accuracy of detection.
In 2012 convolutionalneuralnetworks or CNNs, were used to great success improving dramatically over the previous state-of-the-arts in the ImageNet computer vision compe- tition . Since their success in image recognition CNNs have seen a rise in popularity, finding their way into more complex computer vision challenges such as medical imag- ing. In the past 5 years there has been an increase in the use of CNNs in biological segmentation tasks. These tasks extend across a wide variety of human anatomy. For ex- ample, CNNs have been used for the automated detection of lymph nodes , the segmentation of knee cartilage  and Alzheimer’s detection  to name a few. For this paper we will be focusing on two specific examples of CNN use in medical imaging segmentation:  by Havaei, et al. and  by Kamnitsas, et al. These two papers show different ap- proaches to CNN architectures applied to the segmentation of MRIs. While the results obtained by both approaches are not directly comparable, we will go through what differs between their approaches and where there is some overlap. After discussing their features we will take a look at their
Title: Robust tracking with convolutionalneuralnetworks
Visual object tracking is a process of object(s) localization through the se- quence of images. Many successful trackers use convolutionalneuralnetworks for tracking. These networks are capable of recognizing object features on an abstract level. We can define trackers as short term and long term trackers with the latter having an additional function which reinitializes their track- ing in case of a failure. In our work, we want to improve the tracker that uses convolutionalneural network which is already accurate and fast but does not offer the possibility of a long term tracking. We propose a new tracker with a tracking failure detection. This detection predicts with plausibility if the tracker is tracking the object correctly or incorrectly. Based on implemented failure detection, we implement the object detection which finds the track- ing object on the image and reinitializes the tracker in case of a predicted tracking failure. With these two features implemented, we fulfill the require- ments for the long term tracker. For even more robust long term tracking we propose two methods of updating the template. With the first method, we save templates in an array while with the second method we gradually update the initial template with new examples. We scored the best result in the so far only existing database for long term tracking evaluation. The results are 24% better than those of the currently best published tracker.
Convolutionalneuralnetworks (CNN) have led to many state-of-the-art results spanning through various fields. However, a clear and profound theoretical understanding of the forward pass, the core algorithm of CNN, is still lacking. In parallel, within the wide field of sparse approximation, Convolutional Sparse Coding (CSC) has gained increasing attention in recent years. A theoretical study of this model was recently conducted, establishing it as a reliable and stable alternative to the commonly practiced patch-based processing. Herein, we propose a novel multi-layer model, ML-CSC, in which signals are assumed to emerge from a cascade of CSC layers. This is shown to be tightly connected to CNN, so much so that the forward pass of the CNN is in fact the thresholding pursuit serving the ML-CSC model. This connection brings a fresh view to CNN, as we are able to attribute to this architecture theoretical claims such as uniqueness of the representations throughout the network, and their stable estimation, all guaranteed under simple local sparsity conditions. Lastly, identifying the weaknesses in the above pursuit scheme, we propose an alternative to the forward pass, which is connected to deconvolutional and recurrent networks, and also has better theoretical guarantees.
This paper describes the process of building a cyberbullying intervention interface driven by a machine-learning based text-classification service. We make two main contribu- tions. First, we show that cyberbullying can be identified in real-time before it takes place, with available machine learning and natural language processing tools, in partic- ular convolutionalneuralnetworks. Second, we present a mechanism that provides in- dividuals with early feedback about how other people would feel about wording choices in their messages before they are sent out. This interface not only gives a chance for the user to revise the text, but also provides a system-level flagging/intervention in a situation related to cyberbullying.
We present a general-purpose tagger based on convolutionalneuralnetworks (CNN), used for both composing word vectors and encoding context information. The CNN tagger is robust across different tag- ging tasks: without task-specific tuning of hyper-parameters, it achieves state-of-the- art results in part-of-speech tagging, mor- phological tagging and supertagging. The CNN tagger is also robust against the out- of-vocabulary problem; it performs well on artificially unnormalized texts.
2 The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto https://github.com/mahdihosseini/CONet
Neural Architecture Search (NAS) has shifted network design from using human intuition to leveraging search al- gorithms guided by evaluation metrics. We study channel size optimization in convolutionalneuralnetworks (CNN) and identify the role it plays in model accuracy and com- plexity. Current channel size selection methods are gener- ally limited by discrete sample spaces while suffering from manual iteration and simple heuristics. To solve this, we introduce an efficient dynamic scaling algorithm – CONet – that automatically optimizes channel sizes across net- work layers for a given CNN. Two metrics – “Rank” and