In late 2018, researchers at FAIR published a paper Rethinking ImageNet Pre-training which was subsequently presented in ICCV2019. The paper presented some very interesting results regarding pre-training. I didn’t write a post about it then, but we had a long discussion on it in our KaggleNoobs slack. Researchers at Google…

Have you heard of meta-learning? Do you remember the time when you used pseudo labeling for a Kaggle competition? What if we combine the two techniques? Continuing the series of posts regarding semi-supervised learning, today, we will discuss the latest research paper that aims to combine meta-learning and pseudo labeling…

Semi-supervised learning is finally getting all the attention it deserves. From vision-based tasks to Language Modeling, self-supervised learning has paved a new way of learning (much) better representations. This paper, SimCLR, presents a new framework for contrastive learning of visual representations. Contrastive Learning Before getting into the details of SimCLR, let’s take…

2019 is coming to an end. The landscape of Data Science and Machine Learning has made great progress. From an overwhelming amount of papers to focus on reproducibility and interpretability, this has been an incredible year overall. But today, I am not going to talk about another research paper or…

Object Detection has come a long way. From trivial computer vision techniques for object detection to advanced object detectors, the improvements have been amazing. Convolutional Neural Networks (CNNs) have played a huge role in this revolution. We want our detector to be as accurate as possible as well as fast…

2019 has been the year where a lot of research has been focused on designing efficient deep learning models, self-supervised learning, learning with a limited amount of data, new pruning strategies, etc. …

In recent years, we have witnessed the remarkable achievements of CNNs. Iterative improvements for a task require bigger models and more computation. However, bigger models with huge memory footprints and computation requirements prevent the deployment of these models on mobiles and edge devices. …

In late 2015, a team at Google came up with a paper “Rethinking the Inception Architecture for Computer Vision” where they introduced a new technique for robust modelling. This technique was termed as “Label Smoothing”. Since then this technique has been used in many state-of-the art-models including image classification, language…

Since AlexNet won the 2012 ImageNet competition, CNNs (short for Convolutional Neural Networks) have become the de facto algorithms for a wide variety of tasks in deep learning, especially for computer vision. From 2012 to date, researchers have been experimenting and trying to come up with better and better architectures…