An effective way to practice multiple skills simultaneously — As computer vision engineers, a friend of mine and I were thinking of a new way to improve multiple skills simultaneously which should be effective and time-saving. We came up with the idea of implementing computer vision papers on GitHub. In this short article, I want to talk about all…

CutPaste: self-supervised learning as an improvement for Kernel Density Estimation — Self-supervised learning is one of the most popular fields in modern deep-learning research. As Yann Lecun likes to say self-supervised learning is the dark matter of intelligence and the way to create common sense in AI systems. The ideas and techniques of this paradigm attract many researchers to try and…

Combining classic approaches with deep learning for better representations — Fraud detection in the financial datasets, rare event detection in network traffic, visual image inspection for buildings and road monitoring, and defect detection in production lines: these are very common problems where machine learning techniques for Anomaly Detection can be helpful. In short, anomaly detection is a field of research…

Introduction and overview of few-shot object detection — Deep learning solutions for classification and object detection are state of the art in computer vision and that’s not news anymore. Despite the high accuracy and speed of recent SOTA algorithms, there is one big issue: for a good-performing solution, we need a huge amount of data. In addition, the…

Brief introduction and overview of self-supervised contrastive learning — For the past few years, contrastive learning and self-supervised techniques became a hot topic in computer vision. Many researchers from different AI research labs are working on creating new architectures and exploring the effectiveness of these techniques. In this article, we are going to discuss the advantages and disadvantages of…