In this work, we present a lightweight matting objective decomposition network (MODNet) for portrait matting in real-time with a single input image. MODNet inputs a single RGB image and applies explicit constraints to solve matting sub-objectives simultaneously in one stage. The research paper is accepted at AAAI 2022 conference. Research Continue Reading
Deep Learning
Why Deep Learning is not Artificial General Intelligence (AGI)
With the development in the field of deep learning, it has become a frontier in solving multiple challenging problems in computer vision, games, self-driving cars and many more. Deep learning has even achieved superhuman performance in some tasks, but still, it lacks some fundamental features which are required for a Continue Reading
Deep Learning based Background Removal from Images using TensorFlow and Python
In this tutorial, we are going to learn how to use deep learning to remove background from images with TensorFlow. In short, we’ll use DeepLabV3+, a semantic segmentation based model to extract the background and foreground mask from the image. We are going to use these masks to extract the Continue Reading
Custom Layer in TensorFlow using Keras API
The majority of the people interested in deep learning must have used the TensorFlow library. It is the most popular and widely used deep learning framework. We have used the different layers provided by the tf.keras API to build different types of deep neural networks. But, there are many times Continue Reading
VGG16 UNET Implementation in TensorFlow
In this article, we are going to implement the most widely used image segmentation architecture called UNET. We are going to replace the UNET encoder with the VGG16 implementation from the TensorFlow library. The UNET encoder would learn the features from scratch, while the VGG16 is already trained on the Continue Reading
Squeeze and Excitation Implementation in TensorFlow and PyTorch
The Squeeze and Excitation network is a channel-wise attention mechanism that is used to improve the overall performance of the network. In today’s article, we are going to implement the Squeeze and Excitation module in TensorFlow and PyTorch. What is Squeeze and Excitation Network? The squeeze and excitation attention mechanism Continue Reading
Semi-supervised Learning – Fundamentals of Deep Learning
Semi-supervised learning is a type of machine learning where we use a combination of a large amount of unlabelled data and a small amount of labelled data to train the model. It is a hybrid approach between supervised learning and unsupervised learning. The basic difference between the two is that Continue Reading
Squeeze and Excitation Networks
Convolutional Neural Network (CNN) has been most widely used in the field of computer vision and visual perception to solve multiple tasks such as image classification, semantic segmentation and many more. However, there is a need for approaches that can further improve its performance. One such approach is to add Continue Reading
What is Residual Network or ResNet?
Deep neural networks have become popular due to their high performance in real-world applications, such as image classification, speech recognition, machine translation and many more. Over time deep neural networks are becoming deeper and deeper to solve more complex tasks. Adding more layers to a deep neural network can improve Continue Reading
Supervised vs Unsupervised Learning
In this article, we are going to explore the two machine learning approaches – supervised and unsupervised learning. It is one of the most basic questions for data science beginners. Without a basic understanding of supervised and unsupervised learning, you cannot make any progress in the field of data science. Continue Reading