Why Synthetic Data Helps Document Processing and OCR Models
OCR models need realistic text layout and edge cases. Synthetic data offers a scalable way to generate them without the cost or privacy risks of real documents.
Coasty Blog3 min read
data-augmentation
5 stories
Rare events and edge cases: where synthetic data wins
Training AI on rare failures is hard when they never happen in production. Synthetic data lets you flood the model with edge cases to make it robust.
Coasty Blog2 min readSynthetic Data for Document Processing and OCR Models
Real documents are messy, hard to label, and expensive to acquire. Synthetic data can solve these bottlenecks with realistic variations and zero privacy risk.
Coasty Blog3 min read
Learning with not Enough Data Part 3: Data Generation
Here comes the Part 3 on learning with not enough data (Previous: Part 1 and Part 2). Let’s consider two approaches for generating synthetic data for training. Augmented data.
Lil'Log (Lilian Weng)27 min read
Contrastive Representation Learning
The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings.
Lil'Log (Lilian Weng)41 min read
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