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  1. Deep learning - Wikipedia
  2. Models of Neural Networks IV: Early Vision and Attention
  3. What’s Wrong with Seq2Seq Model?
  4. Applied Optics

Implementing the expert object recognition pathway. Draper BA and Lionelle A. Evaluation of selective attention under similarity transformations. Computer Vision and Image Understanding ; 1 Engel PM. INBC : an incremental algorithm for dataflow segmantation based on a probabilistic approach. Colour tuning in human visual cortex measured with functional magnetic resonance imaging.

Nature ; Frintrop S. VOCUS : a visual attention system for object detection and goal-directed search. Overcomplete steerable pyramid filters and rotation invariance. Harel J and Koch C. On the optimality of spatial attention for object detection. Visual selective attention model for robot vision. Evaluation of visual attention models under 2d similarity transformations. Special Track on Intelligent Robotic Systems.

Active vision using an analog VLSI model of selective attention. Itti L. Models of bottom-up attention and saliency. San Diego: Elsevier Press; Itti L and Koch C. Computational modeling of visual attention. Nature Reviews. A model of saliency-based visual attention for rapid scene nalysis. Color perception. In: Arbib MA. The handbook of brain theory and neural networks. Cambridge: MIT Press; Klein RM. Trends in Cognitive Sciences. Koch C and Ullman S. Shifts in selective visual attention: toward the underlying neural circuitry. Human Neurobiology ; 4 4 Cue-guided search: a computational model of selective attention.

IEEE Trans. Neural Networks ; 16 4 Leventhal AG. The neural basis of visual function. Lindeberg T. Feature detection with automatic scale selection. International Journal of Computer Vision ; 30 2 Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of Computational Neuroscience ; 10 1 Lowe DG. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision ; 60 2 A novel hierarchical framework for object-based visual attention.

An attentiondriven model for similar images with image retrieval applications. Mozer MC and Sitton M. Computational modeling of spatial attention. In: Pashler H. London: Psychology Press, London; Nagai Y. From bottom-up visual attention to robot action learning. Niebur E and Koch C. Control of selective visual attention: modeling the "where" pathway. Neural Information Processing System ; 8 1 Object-based visual attention: a model for a behaving robot. Visual attention-based robot self-localization. Pashler, H. The Psycology of Attention. Integrating visual context and object detection within a probabilistic framework.

Treisman AM. Features and objects: the fourteenth bartlett memorial lecture. The Quarterly Journal of Experimental Psychology ; 40 2 Treisman AM and Gelade G. A feature integration theory of attention. Cognitive Psychology ; 12 1 Modeling visual attention via selective tuning. Vieira-Neto H.

Deep learning - Wikipedia

Visual novelty detection for autonomous inspection robots. Essex: University of Essex ; Vieira-Neto H and Nehmzow U. Visual novelty detection with automatic scale selection. Robotics and Autonomous Systems ; 55 9 A visual brain chip based on selective attention for robot vision application.

Witkin AP. Scale-space filtering. Received: June 17, ; Accepted: August 27, II In grayscale versions of this paper, the dark points in Figures 5c and 5d represent the most conspicuous areas. The images used in the experiments do not have these arrows. All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License. Services on Demand Journal. Introduction The amount of information coming down the optic nerve in the primate's visual system, estimated to be on the order of bits per second, far exceeds what the brain is capable of fully processing and assimilating into conscious experience Related Work The first computational model of visual attention was initially proposed in Koch and Ullman 22 and later improved in Itti et al.

Center-surround operations In the mammalian visual system, many visual neurons are most sensitive in a small region of the visual space, while visual stimuli present in a broader, weaker antagonistic region concentric with the center inhibit the neuronal response Intensity maps The first set of feature maps is concerned with intensity contrast, which in mammals is detected by neurons sensitive either to dark centers on bright surrounds, or bright center on dark surrounds Color maps According to Engel et al.

Four broadly-tuned color channels are created for the red, green, blue and yellow colors 19 : where negative values are set to zero. These scale-spaces are then normalized and combined into a color scale-space C O,S by: Figure 5 shows some examples of color maps. Orientation maps The mammalian visual cortex has neurons which are sensitive to spatial orientation, and according to 5 , the receptive field sensitivity profile of these neurons is approximated by Gabor filters, which are the product of a cosine grating and a 2D Gaussian envelope Saliency map After generating all feature scale-spaces, NLOOK combines them into a unique saliency scale-space S O, S through the normalization and point-by-point addition of the corresponding octaves and scales.

More specifically, for each octave o and scale s the saliency map S O, S is computed by: Figure 7 shows a saliency scale-space S O, S computed from the source image shown in Figure 8b. Scale selection Apart from computing the positions of the most interesting image locations, NLOOK is able to find out the approximate dimensions of this locations, also called the characteristic scale.

According to 40 , a more precise location in scale is determined by interpolation using a second-order Taylor expansion: where s is the scale of the octave in which the extremum was found, and L s and L ss are the first and second partial derivatives of the Laplacian function L relative to the level s, respectively. Inhibition of return The unique saliency map , described above, defines the most salient image location at any given ti m e to which the focus of attention should be directed. Experiments and Results In this section, three sets of experiments have been accomplished to validate the proposed model, and also to compare it with two other publicly available attention models: NVT 19 and SAFE 8.

Experiments using synthetic images According to Draper et al. Applied Optics Vol. Not Accessible Your account may give you access. Abstract A neural network model of selective attention is discussed. Improvement of the generalization capability for a pattern-recognition neural network that uses a Gaussian-synapse neuron model Xin Lin, Masahiko Mori, and Masanobu Watanabe Appl. More Recommended Articles. References You do not have subscription access to this journal.

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Equations displayed with MathJax. Right click equation to reveal menu options. Select as filters. All Rights Reserved. While the algorithm worked, training required 3 days. By such systems were used for recognizing isolated 2-D hand-written digits, while recognizing 3-D objects was done by matching 2-D images with a handcrafted 3-D object model.

Weng et al. Because it directly used natural images, Cresceptron started the beginning of general-purpose visual learning for natural 3D worlds. Cresceptron is a cascade of layers similar to Neocognitron. But while Neocognitron required a human programmer to hand-merge features, Cresceptron learned an open number of features in each layer without supervision, where each feature is represented by a convolution kernel. Cresceptron segmented each learned object from a cluttered scene through back-analysis through the network.

Max pooling , now often adopted by deep neural networks e. ImageNet tests , was first used in Cresceptron to reduce the position resolution by a factor of 2x2 to 1 through the cascade for better generalization. Each layer in the feature extraction module extracted features with growing complexity regarding the previous layer. In , Brendan Frey demonstrated that it was possible to train over two days a network containing six fully connected layers and several hundred hidden units using the wake-sleep algorithm , co-developed with Peter Dayan and Hinton.

Simpler models that use task-specific handcrafted features such as Gabor filters and support vector machines SVMs were a popular choice in the s and s, because of artificial neural network 's ANN computational cost and a lack of understanding of how the brain wires its biological networks. Both shallow and deep learning e. Most speech recognition researchers moved away from neural nets to pursue generative modeling.

An exception was at SRI International in the late s. The speaker recognition team led by Larry Heck achieved the first significant success with deep neural networks in speech processing in the National Institute of Standards and Technology Speaker Recognition evaluation. The principle of elevating "raw" features over hand-crafted optimization was first explored successfully in the architecture of deep autoencoder on the "raw" spectrogram or linear filter-bank features in the late s, [49] showing its superiority over the Mel-Cepstral features that contain stages of fixed transformation from spectrograms.

The raw features of speech, waveforms , later produced excellent larger-scale results. Many aspects of speech recognition were taken over by a deep learning method called long short-term memory LSTM , a recurrent neural network published by Hochreiter and Schmidhuber in In , LSTM started to become competitive with traditional speech recognizers on certain tasks.

In , publications by Geoff Hinton , Ruslan Salakhutdinov , Osindero and Teh [56] [57] [58] showed how a many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine , then fine-tuning it using supervised backpropagation. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision and automatic speech recognition ASR.

The NIPS Workshop on Deep Learning for Speech Recognition [69] was motivated by the limitations of deep generative models of speech, and the possibility that given more capable hardware and large-scale data sets that deep neural nets DNN might become practical. It was believed that pre-training DNNs using generative models of deep belief nets DBN would overcome the main difficulties of neural nets.

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DNN models, stimulated early industrial investment in deep learning for speech recognition, [72] [69] eventually leading to pervasive and dominant use in that industry. That analysis was done with comparable performance less than 1. In , researchers extended deep learning from TIMIT to large vocabulary speech recognition, by adopting large output layers of the DNN based on context-dependent HMM states constructed by decision trees.

Models of Neural Networks IV: Early Vision and Attention

Advances in hardware have enabled renewed interest in deep learning. While there, Andrew Ng determined that GPUs could increase the speed of deep-learning systems by about times. In , a team led by George E. Dahl won the "Merck Molecular Activity Challenge" using multi-task deep neural networks to predict the biomolecular target of one drug. Significant additional impacts in image or object recognition were felt from to In October , a similar system by Krizhevsky et al. In November , Ciresan et al. The Wolfram Image Identification project publicized these improvements.

Image classification was then extended to the more challenging task of generating descriptions captions for images, often as a combination of CNNs and LSTMs. Some researchers assess that the October ImageNet victory anchored the start of a "deep learning revolution" that has transformed the AI industry. In March , Yoshua Bengio , Geoffrey Hinton and Yann LeCun were awarded the Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.

Artificial neural networks ANNs or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn progressively improve their ability to do tasks by considering examples, generally without task-specific programming. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the analytic results to identify cats in other images.

They have found most use in applications difficult to express with a traditional computer algorithm using rule-based programming. An ANN is based on a collection of connected units called artificial neurons , analogous to biological neurons in a biological brain. Each connection synapse between neurons can transmit a signal to another neuron. The receiving postsynaptic neuron can process the signal s and then signal downstream neurons connected to it.

Neurons may have state, generally represented by real numbers , typically between 0 and 1. Neurons and synapses may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream. Typically, neurons are organized in layers.

Different layers may perform different kinds of transformations on their inputs. Signals travel from the first input , to the last output layer, possibly after traversing the layers multiple times. The original goal of the neural network approach was to solve problems in the same way that a human brain would.

Over time, attention focused on matching specific mental abilities, leading to deviations from biology such as backpropagation, or passing information in the reverse direction and adjusting the network to reflect that information. Neural networks have been used on a variety of tasks, including computer vision, speech recognition , machine translation , social network filtering, playing board and video games and medical diagnosis.

As of , neural networks typically have a few thousand to a few million units and millions of connections. Despite this number being several order of magnitude less than the number of neurons on a human brain, these networks can perform many tasks at a level beyond that of humans e. A deep neural network DNN is an artificial neural network ANN with multiple layers between the input and output layers. The network moves through the layers calculating the probability of each output.

For example, a DNN that is trained to recognize dog breeds will go over the given image and calculate the probability that the dog in the image is a certain breed. The user can review the results and select which probabilities the network should display above a certain threshold, etc. Each mathematical manipulation as such is considered a layer, and complex DNN have many layers, hence the name "deep" networks.

DNNs can model complex non-linear relationships. DNN architectures generate compositional models where the object is expressed as a layered composition of primitives. Deep architectures include many variants of a few basic approaches.

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Each architecture has found success in specific domains. It is not always possible to compare the performance of multiple architectures, unless they have been evaluated on the same data sets. DNNs are typically feedforward networks in which data flows from the input layer to the output layer without looping back. At first, the DNN creates a map of virtual neurons and assigns random numerical values, or "weights", to connections between them.

The weights and inputs are multiplied and return an output between 0 and 1.

What’s Wrong with Seq2Seq Model?

If the network did not accurately recognize a particular pattern, an algorithm would adjust the weights. Recurrent neural networks RNNs , in which data can flow in any direction, are used for applications such as language modeling. Convolutional deep neural networks CNNs are used in computer vision.

Two common issues are overfitting and computation time. DNNs are prone to overfitting because of the added layers of abstraction, which allow them to model rare dependencies in the training data. This helps to exclude rare dependencies. DNNs must consider many training parameters, such as the size number of layers and number of units per layer , the learning rate , and initial weights.

Sweeping through the parameter space for optimal parameters may not be feasible due to the cost in time and computational resources. Various tricks, such as batching computing the gradient on several training examples at once rather than individual examples [] speed up computation. Large processing capabilities of many-core architectures such as GPUs or the Intel Xeon Phi have produced significant speedups in training, because of the suitability of such processing architectures for the matrix and vector computations.

Alternatively, engineers may look for other types of neural networks with more straightforward and convergent training algorithms. CMAC cerebellar model articulation controller is one such kind of neural network. It doesn't require learning rates or randomized initial weights for CMAC. The training process can be guaranteed to converge in one step with a new batch of data, and the computational complexity of the training algorithm is linear with respect to the number of neurons involved.

Large-scale automatic speech recognition is the first and most convincing successful case of deep learning. LSTM RNNs can learn "Very Deep Learning" tasks [2] that involve multi-second intervals containing speech events separated by thousands of discrete time steps, where one time step corresponds to about 10 ms. LSTM with forget gates [] is competitive with traditional speech recognizers on certain tasks. The data set contains speakers from eight major dialects of American English , where each speaker reads 10 sentences. More importantly, the TIMIT task concerns phone-sequence recognition, which, unlike word-sequence recognition, allows weak phone bigram language models.

This lets the strength of the acoustic modeling aspects of speech recognition be more easily analyzed. The error rates listed below, including these early results and measured as percent phone error rates PER , have been summarized since The debut of DNNs for speaker recognition in the late s and speech recognition around and of LSTM around , accelerated progress in eight major areas: [10] [75] [73]. All major commercial speech recognition systems e.

Applied Optics

MNIST is composed of handwritten digits and includes 60, training examples and 10, test examples. A comprehensive list of results on this set is available. Deep learning-based image recognition has become "superhuman", producing more accurate results than human contestants. This first occurred in Closely related to the progress that has been made in image recognition is the increasing application of deep learning techniques to various visual art tasks.

DNNs have proven themselves capable, for example, of a identifying the style period of a given painting, b Neural Style Transfer - capturing the style of a given artwork and applying it in a visually pleasing manner to an arbitrary photograph or video, and c generating striking imagery based on random visual input fields. Neural networks have been used for implementing language models since the early s. Other key techniques in this field are negative sampling [] and word embedding. Word embedding, such as word2vec , can be thought of as a representational layer in a deep learning architecture that transforms an atomic word into a positional representation of the word relative to other words in the dataset; the position is represented as a point in a vector space.

Using word embedding as an RNN input layer allows the network to parse sentences and phrases using an effective compositional vector grammar. Recent developments generalize word embedding to sentence embedding. Google Translate GT uses a large end-to-end long short-term memory network. Google Translate supports over one hundred languages. A large percentage of candidate drugs fail to win regulatory approval. These failures are caused by insufficient efficacy on-target effect , undesired interactions off-target effects , or unanticipated toxic effects.

AtomNet is a deep learning system for structure-based rational drug design. In generative neural networks were used to produce molecules that were validated experimentally all the way into mice [] , []. Deep reinforcement learning has been used to approximate the value of possible direct marketing actions, defined in terms of RFM variables.

The estimated value function was shown to have a natural interpretation as customer lifetime value. Recommendation systems have used deep learning to extract meaningful features for a latent factor model for content-based music recommendations. An autoencoder ANN was used in bioinformatics , to predict gene ontology annotations and gene-function relationships. In medical informatics, deep learning was used to predict sleep quality based on data from wearables [] and predictions of health complications from electronic health record data.

Deep learning has been shown to produce competitive results in medical application such as cancer cell classification, lesion detection, organ segmentation and image enhancement [] []. Finding the appropriate mobile audience for mobile advertising is always challenging, since many data points must be considered and assimilated before a target segment can be created and used in ad serving by any ad server.

This information can form the basis of machine learning to improve ad selection. Deep learning has been successfully applied to inverse problems such as denoising , super-resolution , inpainting , and film colorization. These applications include learning methods such as "Shrinkage Fields for Effective Image Restoration" [] which trains on an image dataset, and Deep Image Prior , which trains on the image that needs restoration.