Applying Principles of Quantitative Finance to Computer Vision
Blog post from Encord
The key idea in the market is to capture alpha by collecting risk-adjusted returns above the average market over a certain time scale, which requires identifying predictive signals that can be used to develop trading strategies. To achieve this, quantitative researchers and traders take a modular approach, researching individual alpha signals individually, testing separate hypotheses for each signal, and combining successful ones into a strategy. This approach allows for efficient iteration and adaptation to changing market conditions. Encord's platform takes a similar modular approach to data annotation in the computer vision domain, breaking down the annotation process into smaller micro-models that can be trained on specific data sets and combined to automate comprehensive annotation. The platform is designed to enable flexibility and adaptability in annotating datasets and setting up new projects, reducing iteration times for AI applications. By focusing on improving training data rather than just model parameters or architectures, Encord's platform enables machine learning teams to iterate quickly and effectively, ultimately providing a competitive edge.
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