Steps to perform when your machine learning model overfits in training
Overfitting is a basic problem which could be mitigated at various stages of machine learning project.
All you need to know about Graph Contrastive Learning
The Graph Contrastive Learning aims to learn the graph representation with the help of contrastive learning.
A guide to generating realistic synthetic image datasets with Kubric
Kubric is an open-source Python framework that allows you to create photo-realistic scenes by combining the functions of PyBullet and Blender.
Maintaining A Daily Log Help In Structuring Research Work: Sahana Prabhu, Robert Bosch
Explainability is emerging for many domains such as medical imaging, assisted driving, and manufacturing defect detection.
Complete Guide To AugLy: A Modern Data Augmentation Library
AugLy, a new open source Python library that helps AI researchers to make use and create data augmentations to improve the robustness of their created machine learning models.
Guide To Customized Data Augmentation Using Tensorflow
The recent advancement in deep learning models has been largely attributed to the quantity and diversity of data gathered…
Guide to Google’s STAC: An SSL Framework For Object Detection
The Google Brain team has introduced STAC, semi-supervised learning (SSL) framework to perform object detection in a simplified way
Types of Regularization Techniques To Avoid Overfitting In Learning Models
Regularization is a set of techniques which can help avoid overfitting in neural networks, thereby improving the accuracy of deep learning models when it is fed entirely new data from the problem domain. There are various regularization techniques, some of the most popular ones are — L1, L2, dropout, early stopping, and data augmentation. Why […]
Why Does Image Data Augmentation Work As A Regularizer in Deep Learning?
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 this demonstration.
Hands-on Guide To Albumentation – Python Library for Image Augmentation
In this article, we will learn about an augmentation package for machine learning specifically using the PyTorch framework called Albumentation.
How Data Augmentation Impacts Performance Of Image Classification, With Codes
The article demonstrates how to do data augmentation to increase the size of the data. We will first build a deep learning model without performing augmentation and will compute the accuracy. After which we will build a similar deep learning model after performing augmentation and compute the accuracy. Finally, we will compare the performance of both models.