Semi-supervised Image Classification with Unlabeled Data

Semi-supervised Image Classification with Unlabeled Data

Supervised learning is the key to computer vision and deep learning. However, what happens when you don’t have access to large, human-labeled datasets?

Supervised learning has been at the forefront of research in computer vision and deep learning over the past decade.

In a supervised learning setting, humans are required to annotate a large amount of dataset manually. Then, models use this data to learn complex underlying relationships between the data and label and develop the capability to predict the label, given the data. Deep learning models are generally data-hungry and require enormous amounts of datasets to achieve good performance. Ever-improving hardware and the availability of large human-labeled datasets has been the reason for the recent successes of deep learning.

One major drawback of supervised deep learning is that it relies on the presence of an extensive amount of human-labeled datasets for training. This luxury is not available across all domains as it might be logistically difficult and very expensive to get huge datasets annotated by professionals. While the acquisition of labeled data can be a challenging and costly endeavor, we usually have access to large amounts of unlabeled datasets, especially image and text data. Therefore, we need to find a way to tap into these underused datasets and use them for learning.

Transfer Learning from Pretrained Models

In the absence of large amounts of labeled data, we usually resort to using transfer learning. So what is transfer learning?

Transfer learning means using knowledge from a similar task to solve a problem at hand. In practice, it usually means using as initializations the deep neural network weights learned from a similar task, rather than starting from a random initialization of the weights, and then further training the model on the available labeled data to solve the task at hand.

data analysis

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