The term ‘domain adaptation’ can be interpreted many ways. In this video tutorial, the term refers to encoding an image into a different image. Autoencoders can be tricked by training on one set of images to decode a different set of images. For example, with enough training data and long enough training time, it is possible to artificially generate electron microscopy images using light microscopy input images.
The code from this video is available at: https://github.com/bnsreenu/python_for_microscopists
In an ecosystem that has become increasingly integrated with huge chunks of data and information traveling through the airwaves, Big Data has become irreplaceable for establishments.
From day-to-day business operations to detailed customer interactions, many ventures heavily invest in data sciences and data analysis to find breakthroughs and marketable insights.
Plus, surviving in the current era, mandates taking informed decisions and surgical precision based on the projected forecast of current trends to retain profitability. Hence these days, data is revered as the most valuable resource.
According to a recent study by Sigma Computing , the world of Big Data is only projected to grow bigger, and by 2025 it is estimated that the global data-sphere will grow to reach 17.5 Zettabytes. FYI one Zettabyte is equal to 1 million Petabytes.
Moreover, the Big Data industry will be worth an estimate of $77 billion by 2023. Furthermore, the Banking sector generates unparalleled quantities of data, with the amount of data generated by the financial industry each second growing by 700% in 2021.
In light of this information, let’s take a quick look at some of the ways application monitoring can use Big Data, along with its growing importance and impact.
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Fast setup and slick UIs create incredible first impressions on users. However, enterprise managers are aware of the fact that they are at the tip of the iceberg. One of the features of a SaaS is interoperability, and such aspects are the ones that business owners need to lay a solid foundation.
Are you aware of the term “Software as a Service (SaaS)?” You probably heard it several times, but you may not know what it’s all about. Well, a SaaS, designed by a cloud-based application development company, is a cloud-based service that helps consumers gain access to software applications over the web. These applications remain hosted on the cloud and used for various purposes by companies as well as individuals.
SaaS created by a cloud-based application development company is the best alternative to traditional software installation systems. You may compare it with a TV channel that’s available for subscription. The user connects to a remotely-located base on a central server and uses a license to access data.
In other words, SaaS offers a method of software delivery by which you can access data from any device connected to the internet. Of course, this particular device should have a web browser. Software vendors host everything associated with the application, including servers, code, and databases.
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Java is a commonly used language for web development, especially on the server-side. Java web applications are distributed applications that run on the internet. Web development with Java allows us to create dynamic web pages where users can interact with the interface.
There are various ways through which you can create dynamic web pages in Java. The Java EE (Enterprise Edition) platform provides various Java technologies for web development to developers. Services like distributed computing, web services, etc. are provided by Java EE. Applications can be developed in Java without using any additional scripting language. Let us see how web applications are made via Java.
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There was a time when televisions were just a dream. Then with the new generation, came new technologies and television evolved. Now is the era of big screens and portable devices. Our android TV application development company in the USA provides you with genuine and innovative android tv app development solutions. Our android TV App developers consider your maintenance needs and work hard to feed you the best services available. For more information call us at +18444455767 or email us at firstname.lastname@example.org or Visit: https://sisgain.com/android-tv-application-development
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As we know, in the deep learning world, we might not have sufficient supervised data for training our model. So domain adaptation is a very useful topic to look for. I recently read an ICDM’19 paper “Contradistinguisher for Unsupervised Domain Adaptation” which proposed a direct way for an unsupervised domain adaptation without the need of domain alignment.
In this blog, I will try to give my understanding of this paper in a nutshell. The authors of the paper also released an implementation for this paper.
As we know that in a deep learning world, we might not have sufficient supervised data for training our model. But there may exist another set of data quite similar (having different distribution), for which we have an adequate amount of labeled data present. In that case, it would be great if we can somehow use the model trained on this data for our task. To get a feel of what we are taking about, let’s discuss a small example:
Suppose we have a sentiment analysis task in our hand and we were given the labeled reviews of books for training purposes. Let us have a look at one of the book reviews.
Review-1:_ “This book has an in depth concept building material for the topics it covers. Totally worthy of your time.”_
Now suppose we have to do sentiment analysis task where the reviews are that of furniture. Let us have a look at one of the furniture reviews also.
Review-2:_ “This furniture has great edge finishing and are very comfortable.”_
What we can deduce here is that no furniture review will resemble Review-1, i.e. the sentiment analysis engine been trained on book reviews will work with uncertainty for furniture reviews due to the difference in underlying distribution.
Another similar type of example can also be seen in our classification task on visual data, where we have two different images of same object but both of them comes from different datasets (As given in Fig 1).
Fig 1: On the left is an image of a bicycle shown on an eCommerce website, on the right is an image of a bicycle taken from a mobile camera.
Going back to our previous example, suppose we have our furniture reviews unlabeled, in that case it would be useful if we could somehow use our labeled book review datasets. The sub-discipline which deals with these type of domain transferring is termed as **Domain Adaptation. **Before going further, we need to understand the meaning of Domain.
**Domain **(D) of any set of data is stated by its three properties: Its input feature space (X), label space (Y) and its underlying joint probability distribution.
Fig 2: Representation of a Domain
Domain adaptation consists of two distinct domains i.e. source and target domains with common input and output space having a _Domain shift. _Domain shift is the difference in the joint probability distribution of two domains having common input and output feature space.
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