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Developing Computer Vision Applications in Data Scarce Environments

By December 12, 2023

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Introduction

In today’s digital era, computer vision stands as a transformative technology, driving innovations across diverse sectors including healthcare, automotive, agriculture, and retail. The crux of these advancements hinges on the availability of vast and varied datasets, essential for training accurate and reliable machine learning models. However, not all industries or applications have the luxury of abundant data, creating a challenge for developers and businesses alike. This article delves into challenges and solutions  that facilitate the development of computer vision applications in data scarce environments, ensuring robust performance and delivering tangible business value.

 

Data Scarce Environments

Data scarce environments in computer vision are defined as those circumstances where there is limited or no access to necessary data, a challenge exemplified in biomedical imaging where images of rare abnormalities are scarce and confidential. Such environments, distinct from those with abundant open-source data, often present skewed datasets with an overrepresentation of normal cases. Addressing this requires innovative, ethical approaches like synthetic data generation, transfer learning, and few-shot learning, ensuring the development of reliable and accurate computer vision applications despite these constraints.

 

Handling Challenges

The surge in deep learning's popularity especially for computer vision over the past decade has been paralleled by significant advancements within the field. This progress has generated a wealth of knowledge that is ripe for reuse. Typically, deep learning models are developed using large and diverse datasets, featuring a wide array of shapes, sizes, and patterns. However, the training process for these models is not trivial—it requires substantial capital and sophisticated infrastructure, resources that are not readily available to every company. For organizations lacking these means, there's a viable alternative to building models from the ground up: they can capitalize on pre-trained models. By feeding their unique, albeit smaller, datasets into these advanced models, companies can adopt techniques such as transfer learning, few-shot learning, and self-supervised learning. These strategies offer a way to customize the models to their specific needs without the hefty investment typically associated with deep learning, democratizing access to this cutting-edge technology.

 

Finding Strategic Solutions for Data Scarce Environments

Strategic solutions involve mainly using pre-trained models. As explained above,  these models are already trained on several million data points. They have already learnt several patterns in the data. The next set of solutions will mainly involve tweaking the neural network pre-trained model and solving problems. 

  1. Transfer Learning: This technique entails modifying only the last layer of the neural network model. Typically, the last layer in pre-trained models consists of thousands of classes, which may not align with the specific use case of an industry. By adjusting the final layer, we tailor a generic solution to a more specific one.

For further conceptual understanding this is useful

https://github.com/odpi/OpenDS4All/blob/master/opends4all-resources/opends4all-machine-learning/TRANSFER-LEARNING-Computer-Vision-intermediate.pptx

  1. Few-Shot Learning: Also known as fine-tuning, this approach involves training several layers preceding the last layer. During this process, the intermediate last few layers are usually retrained. While pre-trained models excel at learning external features, they might struggle with internal features specific to the data. Fine-tuning techniques help the model focus more on the specific data, enhancing prediction accuracy.
  2. Self-Supervised Learning: In scenarios where there is an abundance of data lacking curation, especially ground truth labels or annotations necessary for training, self-supervised learning comes into play. This technique enables the model to identify and generate labels using a self-supervised approach, eliminating the need for manual annotation. It also facilitates obtaining labeled data crucial for fine-tuning the models.

 

Limitations

While computer vision models prove invaluable in addressing significant real-world challenges, they are not without their limitations. Understanding the rationale behind a model's specific output can be a complex task, posing a challenge in transparency. Additionally, the issue of false positives introduces a layer of complexity, as models may occasionally produce inaccurate results. It is imperative for companies investing in AI to acknowledge and navigate these challenges effectively. Embracing these considerations ensures robust support for AI initiatives and fosters a proactive approach to addressing potential issues, thereby optimizing the overall success of the technology within the business landscape.

 

Conclusion

Developing computer vision applications in data scarce environments is undeniably challenging, but it is not an insurmountable task. By leveraging synthetic data, utilizing advanced machine learning techniques, and adopting a mindful approach to risk management, businesses can unlock the full potential of computer vision, transforming challenges into opportunities for innovation and growth.

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