The goal of few-shot learning is to learn a model that can recognize novel classes based on one or few training data. It is challenging mainly due to two aspects: (1) it lacks good feature representation of novel classes; (2) a few labeled data could not accurately represent the true data distribution. In this work, we use a sophisticated network architecture to learn better feature representation and focus on the second issue. A novel continual local replacement strategy is proposed to address the data deficiency problem. It takes advantage of the content in unlabeled images to continually enhance labeled ones. Specifically, a pseudo labeling strategy is adopted to constantly select semantic similar images on the fly. Original labeled images will be locally replaced by the selected images for the next epoch training. In this way, the model can directly learn new semantic information from unlabeled images and the capacity of supervised signals in the embedding space can be significantly enlarged. This allows the model to improve generalization and learn a better decision boundary for classification. Extensive experiments demonstrate that our approach can achieve highly competitive results over existing methods on various few-shot image recognition benchmarks.

Over the past few months, I've been working on a fascinating project with one of the world's largest pharmaceutical companies to apply SAS Viya computer vision to help identify potential quality issues on the production line as part of the validated inspection process. As I know the application of these types of AI and ML techniques are of real interest to many high-tech manufacturing organisations as part of their Manufacturing 4.0 initiatives, I thought I'd take the to opportunity to share my experiences with a wide audience, so I hope you enjoy this blog post. For obvious reasons, I can't share specifics of the organisation or product, so please don't ask me to. But I hope you find this article interesting and informative, and if you would like to know more about the techniques then please feel free to contact me. Quality inspections are a key part of the manufacturing process, and while many of these inspections can be automated using a range of techniques, tests and measurements, some issues are still best identified by the human eye.

This dissertation explores the integration of learning and analogy-making through the development of a computer program, called Analogator, that learns to make analogies by example. By "seeing" many different analogy problems, along with possible solutions, Analogator gradually develops an ability to make new analogies. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing research on analogy-making, in which typically the a priori existence of analogical mechanisms within a model is assumed. The present research extends standard connectionist methodologies by developing a specialized associative training procedure for a recurrent network architecture. The network is trained to divide input scenes (or situations) into appropriate figure and ground components. Seeing one scene in terms of a particular figure and ground provides the context for seeing another in an analogous fashion. After training, the model is able to make new analogies between novel situations. Analogator has much in common with lower-level perceptual models of categorization and recognition; it thus serves as a unifying framework encompassing both high-level analogical learning and low-level perception. This approach is compared and contrasted with other computational models of analogy-making. The model's training and generalization performance is examined, and limitations are discussed.

Apple reportedly spent around $200 million to purchase US artificial intelligence startup Xnor.ai, according to GeekWire. The startup's low-power, edge-based AI tools will allow Apple to add AI features to power-constrained devices, like smart cameras or phones. For instance, Xnor.ai's most notable technology is an AI-based image recognition tool that enables on-device human detection for smart home cameras. Apple will also have access to a platform created by Xnor.ai that allows software developers who aren't well-versed in AI to implement AI-related code and data libraries in their apps. Apple's Xnor.ai acquisition is just one of many it has recently made, as it aims to create more powerful and personalized AI features.

Apple has acquired Xnor.ai, a Seattle startup specializing in low-power, edge-based artificial intelligence tools, sources with knowledge of the deal told GeekWire. Speaking on condition of anonymity, sources said Apple paid an amount similar to what was paid for Turi, in the range of $200 million. Xnor.ai didn't immediately respond to our inquiries, while Apple emailed us its standard response on questions about acquisitions: "Apple buys smaller technology companies from time to time and we generally do not discuss our purpose or plans." When we visited Xnor.ai's office in Seattle's Fremont neighborhood this morning, a move was clearly in progress -- presumably to Apple's Seattle offices. The arrangement suggests that Xnor's AI-enabled image recognition tools could well become standard features in future iPhones and webcams.

Image recognition typically is a process of the image processing, identifying people, patterns, logos, objects, places, colors, and shapes, the whole thing that can be sited in the image. And advanced image recognition, in this way, is a framework for employing AI and deep learning that can accomplish greater automation across identification processes. As vision and speech are two crucial human interaction elements, data science is able to imitate these human tasks using computer vision and speech recognition technologies. Even it has already started emulating and has leveraged in different fields, particularly in e-commerce amongst sectors. Advancements in machine learning and the use of high bandwidth data services are fortifying the applications of image recognition.

For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity. FDR algorithms estimate a dense displacement field by interpolating a sparse field, which is given by the established correspondence between selected features. In this paper, we consider the deformation field as a Gaussian Process (GP), whereas the selected features are regarded as prior information on the valid deformations. Using GP, we are able to estimate the both dense displacement field and a corresponding uncertainty map at once. Furthermore, we evaluated the performance of different hyperparameter settings for squared exponential kernels with synthetic, phantom and clinical data respectively. The quantitative comparison shows, GP-based interpolation has performance on par with state-of-the-art B-spline interpolation. The greatest clinical benefit of GP-based interpolation is that it gives a reliable estimate of the mathematical uncertainty of the calculated dense displacement map.

Deep learning is a fascinating sub field of machine learning that creates artificially intelligent systems inspired by the structure and function of the brain. The basis of these models are bio-inspired artificial neural networks that mimic the neural connectivity of animal brains to carry out cognitive functions such as problem solving. A field with the most impressive results of neuromorphic computing is that of visual image analysis. Similar to how our brains learn to recognize objects in order to make predictions and act upon them, artificial intelligence must be shown millions of pictures before they are able to generalize them in order to make their best educated guesses for images they have never seen before. Professor Cheol Seong Hwang from the Department of Material Science and Engineering at Seoul National University and his research team have developed a method to accelerate the image recognition process by combining the inherent efficiency of resistive random access memory (ReRAM) and cross-bar array structures, two of the most commonly used hardware. Many of us have performed a reversed image search to find information based on a certain image in order to browse similar results.

Redmond, Washington and Ithaca, New York – Jenny Sabin is perched high on a scissor lift, her head poking through an opening of the porous fabric structure that she's struggling to stretch onto the exoskeleton of her installation piece, which is suspended in the airy atrium of building 99 on Microsoft's Redmond, Washington, campus. Momentarily defeated, she pauses and looks up. "It's going to be gorgeous," she says. "It" is a glowing, translucent and ethereal pavilion that Sabin and her Microsoft collaborators describe as both a research tool and a glimpse into a future in which architecture and artificial intelligence merge. "To my knowledge, this installation is the first architectural structure to be driven by artificial intelligence in real time," said Sabin, principal designer at Jenny Sabin Studio in Ithaca, New York, who designed and built the pavilion as part of Microsoft's Artist in Residence program.

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