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Fig. 1. Pixel based uncertainty map obtained by the variance of MC dropout method. (Image by author)

Introduction

Active learning algorithms help deep learning engineers select a subset of images from a large unlabeled pool of data in such a way that obtaining annotations of those images will result in a maximal increase of model accuracy. This is the 3rd article in our list of articles about active learning. In our previous articles, we covered 2 algorithms for using active learning on classification models and results of running loss prediction algorithm on human pose estimation. …


Hands-on Tutorials

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Fig. 1: Sorting images based on active learning uncertainty scores (“entropy” values) and assigning the images for annotation with the highest uncertainty. (Image by author)

Introduction

Active learning algorithms help deep learning engineers select a subset of images from a large unlabeled pool of data in such a way that obtaining annotations of those images will result in a maximal increase of model accuracy. This is the 2nd article in our list of articles about active learning. In our previous article, we covered 2 algorithms for using active learning on classification models. In the current article, we will cover the results of using the “Learning Loss for Active Learning” [1] algorithm for object detection and human pose estimation tasks, its usage in SuperAnnotate’s platform, share the code and some benchmarking data. …


Image for post
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Fig. 1: Sorting images based on active learning uncertainty scores (“entropy” values) and assigning the images for annotation with the highest uncertainty. (Image by author)

Introduction

In the last few years, deep learning models achieved groundbreaking results on several computer vision tasks. Yet these models rely on vast amounts of carefully labeled images. The collection of the dataset images is substantially cheaper compared to the price of high-quality annotations. Alternatively, one can collect images from the internet with different tags and use these tags as labels, or crowdsource the annotation process, resulting in much cheaper yet noisier annotations. This motivates the research in directions of unsupervised learning, semi-supervised learning, learning with noisy labels, and active learning.

Active Learning in Computer Vision

Active learning algorithms help deep learning engineers select a subset of images from a large unlabeled pool of data in such a way, that obtaining annotations of those images will result in a maximal increase of model accuracy. They tackle the question of which N images do I annotate next to get the best performing model. This is pretty helpful since in some cases labeling of the whole dataset is impossible due to time and budget constraints. …

About

Martun Karapetyan

ML engineer at SuperAnnotate

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