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Strategy for Minimizing the Labelling Cost, Time and Efforts through Self-Supervised Learning

Hope you all aware that there will be huge cost, efforts and timeline involved in creating a deep learning application or system in the enterprise level with the higher target performance. 

The Conventional Model 

The consideration to bring the balance between the deep learning application performance and the training cost, time and efforts is very much important as there will be huge investments involved from the customer perspective. It will be very difficult to showcase the return of investments (ROI%) over the few years’ time (For E.g., 1 Year, Typically) to the customer who wants to enable the transformation for their specific business applications if we decided to hold huge investment charges for the system training or the data labelling efforts as part of transformation program.

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Bringing Balance between DL system Performance & Labelling Efforts

There are many companies which runs their consumer and business applications over the supervised learning system as a core, since they have enough quality data and the data can capture the entirety of possible scenarios. And, the fields such as the healthcare and Medical where getting enough data is a challenge itself to train the DL application and get the efficiency in terms of F-Score.

The applications start to behave in unpredictable ways when they start seeing the samples to be inferred differ from the training samples.

To avoid the situation mentioned, recommending the Self-Supervised Learning Approach to minimize the Cost, Efforts and Timeline required for the data labelling.

Self-Supervised Learning – The Method 

The diagram represents the method and the various components involved in the self-supervised learning. 

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The Support Pseudo Function represents the image manipulation function with which the data corpus is manipulated and features are getting generated using the supervised approach without human annotators. For E.g., Rotate Image, Sharpen Image etc.

Prediction Using Self-Supervised Model

The diagram represents conventional method which can be used to apply the transfer learning from the features which has been created using the above method on the huge corpus in an unsupervised approach and is integrated with the DL classification model which is trained with lesser image corpus.

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To bring in the following graph true to the business users:

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The above discussed self-supervised learning approach should be the probable way to produce the better time to market and quicker returns of investment in the near time for any kind of AI transformation.

Refer the following materials for the better understanding

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