With images becoming the fastest growing content, image classification has become a major driving force for businesses to speed up processes. Rapid advances in computer vision and ongoing research has allowed enterprises to create solutions that enable automated image tagging and automatically add tags to images to allow users to search and filter more quickly. Enterprises that want to add more value to existing visual content or e-commerce businesses that deal with multiple product photos in a digital asset management DAM system or a web content management WCM environment used by an editorial staff on daily basis rely on image classification techniques to speed up business processes.
The paper presented a new online database, a large-scale ontology of images that offers offers unparalleled opportunities to researchers in the computer vision community and serves as a catalyst for the AI boom. The annual ImageNet image recognition competition has improved the accuracy of classifying images and its winning researchers have donned on senior roles at Google, Baidu and Google-owned London-based DeepMind.
Given the explosion of image data and the application of image classification research in Facebook tagging, land cover classification in agriculture and remote sensing in meterology, oceanography, geology, archaeology and other areas — AI-fuelled research has found a home in everyday applications. In this article, we list down top research papers dealing with convolutional neural networks and their resulting advances in object recognition, image captioning, semantic segmentation and human pose estimation.
AlexNet In fact, marked the first year when a CNN was used to achieve a top 5 test error rate of GoogleNet Over the years, Google has been experimenting with neural networks to improve its image search ability and understand the content within Youtube videos.
Google is leveraging these research advances and converting it into Google products such as in YouTube, image search and even self-driving cars. ZFNet This research paper authored by Matthew D Zeiler and Rob Fergus introduced a novel visualization technique that gave a peek into the functioning of intermediate feature layers and the operation of the classifier.
This architecture was trained on 1. The paper proposed to outperform Krizhevsky on the ImageNet classification benchmark. This research paper, authored by two University of Maryland researchers Rama Chellappa, Swami Sankaranarayanan and GE Global researchers Arpit Jain and Ser Nam Lim proposed a simple learning algorithm that leveraged perturbations of intermediate layer activation to provide a stronger regularization while improving the robustness of deep network to adversarial data.
The research dealt with the behaviour of CNNs as related to adversarial data and the intrigue it had generated in computer vision.
However, the effects of adversarial data on deeper networks had not been explored well. Residual Attention Network for Image Classification Richa Bhatia is a seasoned journalist with six-years experience in reportage and news coverage and has had stints at Times of India and The Indian Express. She is an avid reader, mum to a feisty two-year-old and loves writing about the next-gen technology that is shaping our world.
Richa Bhatia Richa Bhatia is a seasoned journalist with six-years experience in reportage and news coverage and has had stints at Times of India and The Indian Express. Share This. Our Upcoming Events.I am going to be posting some loose notes on different biologically-inspired machine learning lectures. In this note I summarize a talk given in by Geoffrey Hinton where he discusses some shortcomings of convolutional neural networks CNNs.
Convo nets have been remarkably successful. The current deep learning boom can be traced to a paper by Krizhevsky, Sutskever, and Hinton called ImageNet Classification with Deep Convolutional Networks which demonstrated for the first time how a deep CNN could vastly outperform other methods at image classification.
Recently, Hinton expressed deep suspicion about backpropationsaying that he believes it is a very inefficient way of learning, in that it requires a lot of data. Pose information refers to 3D orientation relative to the viewer but also lighting and color. CNNs are known to have trouble when objects are rotated or when lighting conditions are changed. Convolutional networks use multiple layers of feature detectors. Each feature detector is local, so feature detectors are repeated across space.
Pooling gives some translational invariance in much deeper layers, but only in a crude way. According to Hinton, the psychology of shape perception suggests that the human brain achieves translational invariance in a much better way.
This leads to simultanagnosiaa rare neurological condition where patients can only perceive one object at a time. We know that edge detectors in the first layer of the visual cortex V1 do not have translational invariance — each detector only detects things in a small visual field. The same is true in CNNs.
The difference between the brain and a CNN occurs in the higher levels. According to Hinton, CNNs do routing by pooling. Pooling was introduced to reduce redundancy of representation and reduce the number of parameters, recognizing that precise location is not important for object detection. Pooling does routing in a very crude way - for instance max pooling just picks the neuron with the highest activation, not the one that is most likely relevant to the task at hand.
Another difference between CNNs and human vision is the human vision system appears to impose a rectangular coordinate frames on objects. Some simple examples found by the psychologist Irving Rock are as follows:. Very roughly speaking, the square and diamond look like very different shapes, because we represent them in rectangular coordinates. If they were in polar coordinates, they would differ by a single scalar angular phase factor and their numerical representations would be much similar.
The fact the brain embeds things in a rectangular coordinate system means that linear translation is easy for the brain to handle but rotation is hard. Studies have found the mental rotation takes time proportionate to the amount of rotation required.
CNNs cannot handle rotation at all - if they are trained on objects in one orientation, they will have trouble when the orientation is changed. In other words, CNNs could never tell a left shoe from a right shoe, even if they were trained on both. Taking the concept of a capsule further and speaking very hypothetically, Hinton proposes that capsules may be related to cortical minicolumns. Capsules may encode information such as orientation, scale, velocity, and color. Like neurons in the output layer of a CNN, a capsule outputs a probability of whether an entity is present, but additionally has pose metadata attached to it.
This is very useful, because it can allow the brain to figure out if two objects, such as mouth and a nose, are subcomponents of an underlying object a face. Hinton suggests it is easy to determine non-coincidental poses in high dimensions. Hinton says that computer vision should be like inverse graphics. So, while a graphics engine multiplies a rotation matrix times a vector to get the appearance of an object in a particular pose relative to the viewer, a vision system should take appearance and back out the matrix that gives that pose.
Toward the end of the lecture, Hinton shows a system that combines these concepts. Hintons code was written in Matlab and not optimized for speed, so future implementations, especially utilizing GPUs and other parallel hardware, could make them quite competitive with CNNs. Some simple examples found by the psychologist Irving Rock are as follows: Very roughly speaking, the square and diamond look like very different shapes, because we represent them in rectangular coordinates.Empirical studies on Capsule Network representation and improvements implemented with PyTorch.
Another implementation of Hinton's capsule networks in tensorflow. A simple tensorflow implementation of CapsNet by Dr. Hintonbased on my understanding.
This repository is built with an aim to simplify the concept, implement and understand it. The code implements Hinton's matrix capsule with em routing for Cifar dataset.
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CapsNet for NLP.
Updated Jan 12, Python. Updated Jan 22, Python. Updated Jan 28, Python. MXNet implementation of CapsNet. Updated Nov 29, Python.
Updated Mar 16, Python. Updated Feb 19, Python.After a prolonged winter, artificial intelligence is experiencing a scorching summer mainly thanks to advances in deep learning and artificial neural networks. To be more precise, the renewed interest in deep learning is largely due to the success of convolutional neural networks CNNsa neural network structure that is especially good at dealing with visual data. But what if I told you that CNNs are fundamentally flawed?
That was what Geoffrey Hinton, one of the pioneers of deep learningtalked about in his keynote speech at the AAAI conference, one of the main yearly AI conferences. As with all his speeches, Hinton went into a lot of technical details about what makes convnets inefficient—or different—compared to the human visual system. Following is some of the key points he raised. But first, as is our habit, some background on how we got here and why CNNs have become such a great deal for the AI community.
Since the early days of artificial intelligence, scientists sought to create computers that could see the world like humans. The efforts have led to their own field of research collectively known as computer vision. Early work in computer vision involved the use of symbolic artificial intelligencesoftware in which every single rule must be specified by human programmers. The problem is, not every function of the human visual apparatus can be broken down in explicit computer program rules.
The approach ended up having very limited success and use. A different approach was the use of machine learning. Contrary to symbolic AI, machine learning algorithms are given a general structure and unleashed to develop their own behavior by examining training examples. However, most early machine learning algorithms still required a lot of manual effort to engineers the parts that detect relevant features in images. Convolutional neural networks, on the other hand, are end-to-end AI models that develop their own feature-detection mechanisms.
A well-trained CNN with multiple layers automatically recognizes features in a hierarchical way, starting with simple edges and corners down to complex objects such as faces, chairs, cars, dogs, etc.
But because of their immense compute and data requirements, they fell by the wayside and gained very limited adoption. It took three decades and advances in computation hardware and data storage technology for CNNs to manifest their full potential. Today, thanks to the availability of large computation clusters, specialized hardware, and vast amounts of data, convnets have found many useful applications in image classification and object recognition.
One of the key challenges of computer vision is to deal with the variance of data in the real world. Our visual system can recognize objects from different angles, against different backgrounds, and under different lighting conditions. Creating AI that can replicate the same object recognition capabilities has proven to be very difficult. This means that a well-trained convnet can identify an object regardless of where it appears in an image. One approach to solving this problem, according to Hinton, is to use 4D or 6D maps to train the AI and later perform object detection.
For the moment, the best solution we have is to gather massive amounts of images that display each object in various positions. Then we train our CNNs on this huge dataset, hoping that it will see enough examples of the object to generalize and be able to detect the object with reliable accuracy in the real world.
Datasets such as ImageNet, which contains more than 14 million annotated images, aim to achieve just that. In fact, ImageNet, which is currently the go-to benchmark for evaluating computer vision systems, has proven to be flawed. Despite its huge size, the dataset fails to capture all the possible angles and positions of objects.Ball tree algorithm
It is mostly composed of images that have been taken under ideal lighting conditions and from known angles. This is acceptable for the human vision system, which can easily generalize its knowledge. In fact, after we see a certain object from a few angles, we can usually imagine what it would look like in new positions and under different visual conditions.
In effect, the CNN will be trained on multiple copies of every image, each being slightly different. This will help the AI better generalize over variations of the same object.
Data augmentation, to some degree, makes the AI model more robust. There have been efforts to solve this generalization problem by creating computer vision benchmarks and training datasets that better represent the messy reality of the real world.
And those new situations will befuddle even the largest and most advanced AI system. From the points raised above, it is obvious that CNNs recognize objects in a way that is very different from humans. But these differences are not limited to weak generalization and the need for many more examples to learn an object.The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units GPUs during training.
Chellapilla et al. According to the AlexNet paper,  Ciresan's earlier net is "somewhat similar. Weng's method called max-pooling. AlexNet contained eight layers; the first five were convolutional layers, some of them followed by max-pooling layers, and the last three were fully connected layers. AlexNet is considered one of the most influential papers published in computer vision, having spurred many more papers published employing CNNs and GPUs to accelerate deep learning.
Alex Krizhevsky born in Ukraineraised in Canada is a computer scientist most noted for his work on artificial neural networks and deep learning. From Wikipedia, the free encyclopedia. Retrieved 5 October Communications of the ACM. In Lorette, Guy ed. Gambardella; Jurgen Schmidhuber Retrieved 17 November Retrieved Retrieved 14 January Multi-column deep neural networks for image classification. LeCun, B. Boser, J. Denker, D.Zimbra apache log
Henderson, R. Howard, W. Hubbard, L. Proceedings of the IEEE. Retrieved October 7, Bibcode : SchpJ Biological Cybernetics. Retrieved 16 November GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. In NovemberG. Hinton et al. This paper promises a breakthrough in the deep learning community.
This new type of neural network CapsNet is based on the so-called "Capsules". CapsNet enables new applications, especially, it can overcome the main drawback of CNNs. CapsNet is not sensible to linear operations, i. Moreover, unlike CNNs, CapsNet can take into account orientations and spatial relationships between features. In second part, the project aims to go further with one potential application in finance: the time-series bi-labels classification problem.
In this part, results of the paper are reproduced. Then, the reconstruction part of images is highlighted and the Capsnet capacity to identify over-lapped digits is also tested. The reconstruction of input image was a success. Capsnet demonstrated the capacity to identify overlapped digits. In finance, and especially in time-series problems, the time is an important component to take into account. Because of the Capsnet's capacity to consider spatial relationships between features.
The project aimes to explore the application of Capsules for time-series classification problem. The goal of the algorithm is to predict, for a given stock, the sign of the next day return. The architecture of the network is modified because of the nature of the input and output and also to reduce the observed CapsNet tendency to overfit.
The project introduces the usage of dropout in CapsNet, still in order to reduce overfitting. The experiment was run with auto-regressive entry. It is not taking into account the relations between the different stocks.
Exploring this way should lead to better results. It should be interesting because of the CapsNet capacity to identify orientation and spatial relations. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. No description, website, or topics provided. Jupyter Notebook Python. Jupyter Notebook Branch: master. Find file. Sign in Sign up.CNN As police across the US brace for continued emergency calls in the wake of the coronavirus outbreak, one Oregon police department is dealing with calls for an entirely different type of emergency: Residents are calling because they've run out of toilet paper.
Chat with us in Facebook Messenger. Find out what's happening in the world as it unfolds. More Videos Crackdown on coronavirus price gouging? How to clean household surfaces with soap and water.
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Oz citing new study on Fox News. His dream college is on hold because mom lost her job. How coronavirus is redefining the college experience. Doctor: We're lost without widespread Covid testing. Tiny Louisiana parish has highest Covid death rate in US.
Kellyanne Conway makes false claim on Fox about Covid Governor fires back at Trump: Testing is a quagmire. Bishop's daughter on virus: Unfair to say dad 'didn't care'. Chris Cuomo announces wife has virus: It breaks my heart. See residents protest quarantine guidance in Michigan. Waiting for stimulus checks is 'life and death' for some. Los Angeles mayor says large gatherings unlikely until The Newport Police Department put out a notice on Facebook urging residents to stop making emergency calls due to a toilet paper shortage.
You will survive without our assistance. Toilet paper is unavailable at many stores and supermarkets as people across the US stock up on household essentials due to fears over the coronavirus outbreak. Many sellers on Amazon are also out of stock. The psychology behind why toilet paper, of all things, is the latest coronavirus panic buy. The police offered up some humorous, friendly tips for those that are dealing with the shortage. Ancient Romans used a sea sponge on a stick, also soaked in salt water.
We are a coastal town. We have an abundance of salt water available. Sea shells were also used. The police also suggested using receipt papers, newspapers, cloth rags and even an empty toilet paper roll.
There is a TP shortage.Colorbond fascia capping
This too shall pass.
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