We introduce a new deep convolutional neural network, CrescendoNet, by stacking simple building blocks without residual connections. Each Crescendo block contains independent convolution paths with increased depths. The numbers of convolution layers and parameters are only increased linearly in Crescendo blocks. In experiments, CrescendoNet with only 15 layers outperforms almost all networks without residual connections on benchmark datasets, CIFAR10, CIFAR100, and SVHN. Given sufficient amount of data as in SVHN dataset, CrescendoNet with 15 layers and 4.1M parameters can match the performance of DenseNet-BC with 250 layers and 15.3M parameters. CrescendoNet provides a new way to construct high performance deep convolutional neural networks without residual connections. Moreover, through investigating the behavior and performance of subnetworks in CrescendoNet, we note that the high performance of CrescendoNet may come from its implicit ensemble behavior, which differs from the FractalNet that is also a deep convolutional neural network without residual connections. Furthermore, the independence between paths in CrescendoNet allows us to introduce a new path-wise training procedure, which can reduce the memory needed for training.

Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabelled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabelled input data from a latent representation space. More robust representations may be produced by an autoencoder if it learns to recover clean input samples from corrupted ones. Representations may be further improved by introducing regularisation during training to shape the distribution of the encoded data in latent space. We suggest denoising adversarial autoencoders, which combine denoising and regularisation, shaping the distribution of latent space using adversarial training. We introduce a novel analysis that shows how denoising may be incorporated into the training and sampling of adversarial autoencoders. Experiments are performed to assess the contributions that denoising makes to the learning of representations for classification and sample synthesis. Our results suggest that autoencoders trained using a denoising criterion achieve higher classification performance, and can synthesise samples that are more consistent with the input data than those trained without a corruption process.

Join us at the 4th AI NEXTCon, the leading technology conference for AI hosted around the world. AI NEXTCon Seattle brings together top technical engineers, practitioners, influential technologists and data scientists to share solutions and practical experiences in machine/deep learning, computer vision, speech recognition and NLP. The conference features a blend of hands-on workshops, inspirational keynotes, deep dive tech talks, and networking opportunity with like-minded colleagues. Here are just a few of the reasons why you cannot miss this must-attend event: Speakers: 50 speakers from companies like Microsoft, Google, Facebook, Amazon, Uber, Airbnb, LinkedIn, Pinterest, Twitter, NVidia, and more. Topics: 50 deep dive tech talks and practical experiences in machine learning, deep learning, computer vision, speech recognition, neuron network, reinforced learning.

In this post we are going to develop a Cat&Dog Recognizer Java Application using deeplearning4j.If you would like to experiment on your own cat or dog feel free to check out the source code or download the application(fairly short instructions at the end). Although with the great progress of deep learning, computer vision problems tend to be hard to solve. One of the reason is because Neural Networks(NN) are trying to learn a highly complex function like Image Recognition or Image Object Detection. We have a bunch of pixels values and from there we would like to figure out what is inside, so this really is a complex problem on his own. Another reason why even today Computer Vision struggle is the amount of date we have.

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A couple years ago you may have read this great post from boredpanda talking about a research paper that took deep learning algorithms and applied them to art. This opened up the possibility of, say, taking a photo and having it re-imagined as being a painting from an old master. It's actually pretty easy to do this now using a site called deepdreamgenerator.com. I've done quite a few experiments on the site using a variety of images from the web and found it pretty fun. I've also started to download some of the deep learning toolkits (e.g.

Across every industry, organizations are moving aggressively to adopt AI ML DL tools and frameworks to help them become more effective in leveraging data and analytics to power their key business and operational use cases. To help our clients exploit the business and operational benefits of AI ML DL, Dell EMC has created

In the past several years, we've witnessed how deep learning, specifically convolutional neural networks, has been successfully applied to computer vision, natural language processing, speech recognition, logistics, online advertising, and many other problem domains. There are a few things that are unique about the application of deep learning to computer vision and understanding these characteristics will help in understanding the state of computer vision. In this article, I'd like to share a nice summary of the state of computer vision from Course 4 "Convolutional Neural Networks" from the new Deep Learning Specialization series on Coursera. Dr. Andrew Ng provides some compelling observations about deep learning and computer vision with the goal of mapping out the future of this increasingly popular technology. Consider that many machine learning problems fall somewhere on the spectrum between where you're working with "small data" to where you have "big data." For example, there is a decent amount of data available for speech recognition.

This story was first published at The Aleph Report. If you want to read the latest reports, please subscribe to our newsletter and our Twitter. Those that follow technology closely have noticed a significant trend in the field, China. The more you read, the more you encounter increasing coverage on China's tech dealings. In the last few weeks, we've seen Tencent's market capital surpass that of Facebook; Venture Capital activity reach US VC levels; LinkedIn's Chinese rival MaiMai, outperform LinkedIn in the country and the amount of Chinese Women in Tech pass that of the US.