Adversarial learning is one of the most promising ways to train and secure robust deep learning networks. Transfer learning is a critical approach that enables training deep neural networks (DNN) faster and with a relatively lesser amount of data than training from scratch.
In this article, we list down the top 8 Adversarial Methods one can use for 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.
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The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
By Dongsuk Hong
About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
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Project walkthrough on Convolution neural networks using transfer learning
From 2 years of my master’s degree, I found that the best way to learn concepts is by doing the projects. Let’s start implementing or in other words learning.
Take an image as input and return a corresponding dog breed from 133 dog breed categories. If a dog is detected in the image, it will provide an estimate of the dog’s breed. If a human is detected, it will give an estimate of the dog breed that is most resembling the human face. If there’s no human or dog present in the image, we simply print an error.
Let’s break this problem into steps
For all these steps, we use pre-trained models.
Pre-trained models are saved models that were trained on a huge image-classification task such as Imagenet. If these datasets are huge and generalized enough, the saved weights can be used for multiple image detection task to get a high accuracy quickly.
For detecting humans, OpenCV provides many pre-trained face detectors. We use OpenCV’s implementation of Haar feature-based cascade classifiers to detect human faces in images.
### returns "True" if face is detected in image stored at img_path def face_detector(img_path): img = cv2.imread(img_path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray) return len(faces) > 0
For detecting dogs, we use a pre-trained ResNet-50 model to detect dogs in images, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks.
from keras.applications.resnet50 import ResNet50 ### define ResNet50 model ResNet50_model_detector = ResNet50(weights='imagenet') ### returns "True" if a dog is detected def dog_detector(img_path): prediction = ResNet50_predict_labels(img_path) return ((prediction <= 268) & (prediction >= 151))
For classifying Dog breeds, we use transfer learning
Transfer learning involves taking a pre-trained neural network and adapting the neural network to a new, different data set.
To illustrate the power of transfer learning. Initially, we will train a simple CNN with the following architecture:
Train it for 20 epochs, and it gives a test accuracy of just 3% which is better than a random guess from 133 categories. But with more epochs, we can increase accuracy, but it takes up a lot of training time.
To reduce training time without sacrificing accuracy, we will train the CNN model using transfer learning.
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Technology has taken a place of more productiveness and give the best to the world. In the current situation, everything is done through the technical process, you don’t have to bother about doing task, everything will be done automatically.This is an article which has some important technologies which are new in the market are explained according to the career preferences. So let’s have a look into the top trending technologies followed in 2021 and its impression in the coming future in the world.
First in the list of newest technologies is surprisingly Data Science. Data Science is the automation that helps to be reasonable for complicated data. The data is produces in a very large amount every day by several companies which comprise sales data, customer profile information, server data, business data, and financial structures. Almost all of the data which is in the form of big data is very indeterminate. The character of a data scientist is to convert the indeterminate datasets into determinate datasets. Then these structured data will examine to recognize trends and patterns. These trends and patterns are beneficial to understand the company’s business performance, customer retention, and how they can be enhanced.
Next one is DevOps, This technology is a mixture of two different things and they are development (Dev) and operations (Ops). This process and technology provide value to their customers in a continuous manner. This technology plays an important role in different aspects and they can be- IT operations, development, security, quality, and engineering to synchronize and cooperate to develop the best and more definitive products. By embracing a culture of DevOps with creative tools and techniques, because through that company will gain the capacity to preferable comeback to consumer requirement, expand the confidence in the request they construct, and accomplish business goals faster. This makes DevOps come into the top 10 trending technologies.
Next one is Machine learning which is constantly established in all the categories of companies or industries, generating a high command for skilled professionals. The machine learning retailing business is looking forward to enlarging to $8.81 billion by 2022. Machine learning practices is basically use for data mining, data analytics, and pattern recognition. In today’s scenario, Machine learning has its own reputed place in the industry. This makes machine learning come into the top 10 trending technologies. Get the best machine learning course and make yourself future-ready.
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Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.
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