Vaughn  Sauer

Vaughn Sauer

1624047600

ELI5: A Simple Framework for Contrastive Learning of Visual Representations

This article will be a series of ELI5s I will be bringing about to explain papers that led to some of my best insights in 2020. Each article will be a <five-minute read highlighting the key, advantages, and drawbacks while making giving the user the understanding he needs (and “deserves”) to get hacking on their own.

A Simple Framework for Contrastive Learning of Visual Representations or just SimCLR has arguably been one of the best papers in 2020 for me. The reason? Extreme simplicity and maximum impact. So, let’s dive deeper!

This is it. This 10s gif (pronounced gif or gif??) is it! While being this simple, its impact has been EXCEPTIONAL in terms of the performance it could give with a small amount of labeled data.

So, here’s how it works: What happens in this process is, we are taking only the picture(no labels) of the dog and also that of a chair. Then we generate two different images from each image by applying random augmentations on the original ones. For instance, I randomly crop out the picture of the dog, apply a color jitter, and some random color distortion to get the image on the far left. These images are then passed through a CNN, which is my encoder block and these networks output a certain representation for each of these images. These representations that are of size 2048 are further mapped to a latent space of a 128-dimensional vector using an MLP projection head. Now here comes the magic, two transforms of the picture of the dog are made similar using Contrastive Loss like they attract each other whereas the images of the dog and the chair are made to repel each other since they are negative pairs. Contrastive Loss(NT-Xent) in this case is nothing but a glorified Cross-Entropy Loss with Temperature Scaling to help the model learn hard negative features (to help differentiate a dog from an elephant, chair, and a spaceship.

#artificial-intelligence #computer-vision #machine-learning #deep-learning

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ELI5: A Simple Framework for Contrastive Learning of Visual Representations
Vaughn  Sauer

Vaughn Sauer

1624047600

ELI5: A Simple Framework for Contrastive Learning of Visual Representations

This article will be a series of ELI5s I will be bringing about to explain papers that led to some of my best insights in 2020. Each article will be a <five-minute read highlighting the key, advantages, and drawbacks while making giving the user the understanding he needs (and “deserves”) to get hacking on their own.

A Simple Framework for Contrastive Learning of Visual Representations or just SimCLR has arguably been one of the best papers in 2020 for me. The reason? Extreme simplicity and maximum impact. So, let’s dive deeper!

This is it. This 10s gif (pronounced gif or gif??) is it! While being this simple, its impact has been EXCEPTIONAL in terms of the performance it could give with a small amount of labeled data.

So, here’s how it works: What happens in this process is, we are taking only the picture(no labels) of the dog and also that of a chair. Then we generate two different images from each image by applying random augmentations on the original ones. For instance, I randomly crop out the picture of the dog, apply a color jitter, and some random color distortion to get the image on the far left. These images are then passed through a CNN, which is my encoder block and these networks output a certain representation for each of these images. These representations that are of size 2048 are further mapped to a latent space of a 128-dimensional vector using an MLP projection head. Now here comes the magic, two transforms of the picture of the dog are made similar using Contrastive Loss like they attract each other whereas the images of the dog and the chair are made to repel each other since they are negative pairs. Contrastive Loss(NT-Xent) in this case is nothing but a glorified Cross-Entropy Loss with Temperature Scaling to help the model learn hard negative features (to help differentiate a dog from an elephant, chair, and a spaceship.

#artificial-intelligence #computer-vision #machine-learning #deep-learning

Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Jerad  Bailey

Jerad Bailey

1598891580

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Roberta  Ward

Roberta Ward

1595344320

Wondering how to upgrade your skills in the pandemic? Here's a simple way you can do it.

Corona Virus Pandemic has brought the world to a standstill.

Countries are on a major lockdown. Schools, colleges, theatres, gym, clubs, and all other public places are shut down, the country’s economy is suffering, human health is on stake, people are losing their jobs and nobody knows how worse it can get.

Since most of the places are on lockdown, and you are working from home or have enough time to nourish your skills, then you should use this time wisely! We always complain that we want some ‘time’ to learn and upgrade our knowledge but don’t get it due to our ‘busy schedules’. So, now is the time to make a ‘list of skills’ and learn and upgrade your skills at home!

And for the technology-loving people like us, Knoldus Techhub has already helped us a lot in doing it in a short span of time!

If you are still not aware of it, don’t worry as Georgia Byng has well said,

“No time is better than the present”

– Georgia Byng, a British children’s writer, illustrator, actress and film producer.

No matter if you are a developer (be it front-end or back-end) or a data scientisttester, or a DevOps person, or, a learner who has a keen interest in technology, Knoldus Techhub has brought it all for you under one common roof.

From technologies like Scala, spark, elastic-search to angular, go, machine learning, it has a total of 20 technologies with some recently added ones i.e. DAML, test automation, snowflake, and ionic.

How to upgrade your skills?

Every technology in Tech-hub has n number of templates. Once you click on any specific technology you’ll be able to see all the templates of that technology. Since these templates are downloadable, you need to provide your email to get the template downloadable link in your mail.

These templates helps you learn the practical implementation of a topic with so much of ease. Using these templates you can learn and kick-start your development in no time.

Apart from your learning, there are some out of the box templates, that can help provide the solution to your business problem that has all the basic dependencies/ implementations already plugged in. Tech hub names these templates as xlr8rs (pronounced as accelerators).

xlr8rs make your development real fast by just adding your core business logic to the template.

If you are looking for a template that’s not available, you can also request a template may be for learning or requesting for a solution to your business problem and tech-hub will connect with you to provide you the solution. Isn’t this helpful 🙂

Confused with which technology to start with?

To keep you updated, the Knoldus tech hub provides you with the information on the most trending technology and the most downloaded templates at present. This you’ll be informed and learn the one that’s most trending.

Since we believe:

“There’s always a scope of improvement“

If you still feel like it isn’t helping you in learning and development, you can provide your feedback in the feedback section in the bottom right corner of the website.

#ai #akka #akka-http #akka-streams #amazon ec2 #angular 6 #angular 9 #angular material #apache flink #apache kafka #apache spark #api testing #artificial intelligence #aws #aws services #big data and fast data #blockchain #css #daml #devops #elasticsearch #flink #functional programming #future #grpc #html #hybrid application development #ionic framework #java #java11 #kubernetes #lagom #microservices #ml # ai and data engineering #mlflow #mlops #mobile development #mongodb #non-blocking #nosql #play #play 2.4.x #play framework #python #react #reactive application #reactive architecture #reactive programming #rust #scala #scalatest #slick #software #spark #spring boot #sql #streaming #tech blogs #testing #user interface (ui) #web #web application #web designing #angular #coronavirus #daml #development #devops #elasticsearch #golang #ionic #java #kafka #knoldus #lagom #learn #machine learning #ml #pandemic #play framework #scala #skills #snowflake #spark streaming #techhub #technology #test automation #time management #upgrade

Larry  Kessler

Larry Kessler

1617447300

Deep Reinforcement Learning and Representation Learning

One major problem of current state-of-the-art Reinforcement Learning (RL) algorithms is still the need for millions of training examples to learn a good or near-optimal policy to solve the given task. This plays especially a critical role for real-world applications in the industry be it for robotics or other complex optimization problems for decision making or optimal control.
Due to these problems, engineers and researchers are looking for ways to improve this sample-inefficiency to increase the speed of learning and the need for gathering millions of expensive training examples.

#reinforcement-learning #artificial-intelligence #representation-learning #machine-learning