Hello,
Data Purchasing Challenge Round 2 is going strong 
To help you make the most of the challenge, we have released a new baseline containing fast heuristic implementations of some simple ideas.
Need some new ideas? Check out these excellent resources created by the community 
Under the spotlight: Helpful resources created by challenge participants
If these notebooks and explainers help you, don’t forget to 
the notebook and leave a comment.
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Representation Learning: In his notebook, aorhan explains how Representation Learning can be utilised for data label purchase aka Active Learning (AL). You can find the complete explainer over here.
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Explainability: How does your model actually learn? Another notebook by aorhan, explain how given a sample you can identify what features contribute to the decision. And how you can improve your model. Read the notebook over here.
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Purchase with data anamoly: In this notebook, moto shows how to use anomaly scores to select images to buy. His notebook explains the approach and the result of his experiments.
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Labels co-occurence & Image Similarity: In this explainer santiactics performs label co-occurence analysis & Image Similarity using image embeddings. Read the notebook over here.
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Sneak peek into the image sample of Round 2: Taking a data visualisation approach sagar_rathod notebook visualises images from different classes and combinations of them. Complete notebook over here.
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Additional Resources from Challenge Organisers and Top Participants: We recently hosted a live Town Hall event where the organisers and participants shared their ideas. You can find the recording & resource compilation over here.
Don’t forget to submit your own notebook or resource for the Community Contribution Prize.
Do you have questions about the notebooks & baseline? Drop a comment on this thread to them answered quickly. What approach will you try? Let us know 