Transfer Learning and Data Augmentation applied to the Simpsons Image Dataset

In the ideal scenario for Machine Learning (ML), there are abundant labeled training instances, which share the same distribution as the test data [1]. However, these data can be resource-intensive or unrealistic to collect in certain scenarios. Thus, Transfer Learning (TL) becomes a useful approach. It consists of increasing the learning ability of a model by transferring information from a different but related domain. In other words, it relaxes the hypothesis that the training and testing data are independent and identically distributed [2]. It only works if the features that are intended to be learned are general to both tasks. Another method to work with limited data is by using Data Augmentation (DA). It consists of applying a suite of transformations to inflate the dataset. Traditional ML algorithms rely significantly on feature engineering, while Deep Learning (DL) focuses on learning data by unsupervised or semi-supervised feature learning methods and hierarchical feature extraction. DL often requires massive amounts of data to be trained effectively, making it a strong candidate for TL and DA.

#computer-vision #tensorflow #data-augmentation #transfer-learning #deep-learning

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Transfer Learning and Data Augmentation applied to the Simpsons Image Dataset
Jolie  Reichert

Jolie Reichert

1599215220

Why Does Image Data Augmentation Work As A Regularizer in Deep Learning?

The problem with deep learning models is they need lots of data to train a model. There are two major problems while training deep learning models is overfitting and underfitting of the model. Those problems are solved by data augmentation is a regularization technique that makes slight modifications to the images and used to generate data.

In this article, we will demonstrate why data augmentation is known as a regularization technique. How to apply data augmentation to our model and whether it is used as a preprocessing technique or post-processing techniques…? All these questions are answered in the below demonstration.

Topics that we will demonstrate in this article:-

  • Data augmentation as a regularizer and data generator.
  • Implementing Data augmentation techniques.

Data Augmentation As a Regularizer and Data Generator

The regularization is a technique used to reduce the overfitting in the model. unnecessarily. In dealing with deep learning models, too much learning is also bad for the model to make a prediction with unseen data. If we get good results in training data and poor results in unseen data (test data, validation data) then it is framed as an overfitting problem. So now using data augmentation, we perform few transformations to the data like flipping, cropping, adding noise to the data, etc.

As you know, deep learning models are data hungry, if we are lacking data then by using data augmentation transformations of the image we can generate data. Data augmentation is a preprocessing technique because we only work on the data to train our model. In this technique, we generate new instances of images by cropping, flipping, zooming, shearing an original image. So, whenever the training lacks the image dataset, using augmentation, we can create thousands of images to train the model perfectly.


#developers corner #computer vision #data augmentation #deep learning #image augmentation #image data augmentation #image processing #overfitting

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

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Transfer Learning and Data Augmentation applied to the Simpsons Image Dataset

In the ideal scenario for Machine Learning (ML), there are abundant labeled training instances, which share the same distribution as the test data [1]. However, these data can be resource-intensive or unrealistic to collect in certain scenarios. Thus, Transfer Learning (TL) becomes a useful approach. It consists of increasing the learning ability of a model by transferring information from a different but related domain. In other words, it relaxes the hypothesis that the training and testing data are independent and identically distributed [2]. It only works if the features that are intended to be learned are general to both tasks. Another method to work with limited data is by using Data Augmentation (DA). It consists of applying a suite of transformations to inflate the dataset. Traditional ML algorithms rely significantly on feature engineering, while Deep Learning (DL) focuses on learning data by unsupervised or semi-supervised feature learning methods and hierarchical feature extraction. DL often requires massive amounts of data to be trained effectively, making it a strong candidate for TL and DA.

#computer-vision #tensorflow #data-augmentation #transfer-learning #deep-learning

Create Your Own Real Image Dataset with python (Deep Learning)

We have all worked with famous Datasets like CIFAR10 , MNIST , MNIST-fashion , CIFAR100, ImageNet and more. But , what about working on projects with custom made datasets according to your own needs. This also essentially makes you a complete master when it comes to handling image data

most of us probably know how to handle and store numerical and categorical data in csv files. But, the idea of storing Image data in files is very uncommon. Having said that , let’s see how to make our own image dataset with python

Code Begins Here :

1)Let’s start by importing the necessary libraries

#importing the libraries
import os 
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
  1. Then , we need to set the path to the folder or directory that contains the image files. Here, the pictures that I need to upload are being stored in the path mentioned below
#setting the path to the directory containing the pics
path = '/media/ashwinhprasad/secondpart/pics'

#image-dataset #machine-learning-datasets #own-image-dataset #real-data #deep learning