Should you annotate your data?
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Introduction to Image Segmentation - II
We looked at the process of image segmentation and its different types in my previous blog. In this post, we are going to take a deeper look at the architecture of U-Net. We will also discuss the PyTorch-based code for U-Net. The complete code can be found at github.
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Introduction to Image Segmentation - I
The object detection and classification task make the most buzz in the field of computer vision. But one of the most important tasks in computer vision is segmentation. In this blog, I’ll be detailing the application of segmentation-related tasks and in my next blog, I’ll introduce you to an instrumental...
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SimCLR and Contrastive Learning
Usually, when we train a model on a classification task, we are making the model learn the distinction between classes inadvertently. However, we end up optimizing for learning global features that are useful for a high-level understanding. These models would typically fail to generalize for tasks for which they are...
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Self-training and the curious case of Noisy Student
One of the best papers I came across in 2020 was the Google Brain team’s Self-training with Noisy Student. It is a semi-supervised technique that works well when we have an insufficient amount of labeled data. So much so that it beat the contemporary SOTA by a whole 2% gain...
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Exploring active learning - II
I have given a loose introduction to active learning in my previous post.. In this post, I’ll dig deeper into the application of active learning in computer vision.
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Exploring active learning - I
Supervised learning is great. You provide data and labels and the model will be trained. The state of the art in computer vision is almost solely based on supervised learning. But there’s a cost that’s associated with labeling of data that usually simplified in terms of time and monetary resources...
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Deep Learning vs Computer Vision
I am compelled to write this post due to the various trends that I observed and the presence of misinformation related to computer vision and deep learning. This post is aimed at professionals and students who are developing expertise in deep learning and are keen to start a career in...
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