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    Experiment List
    • Turbo Image Filter This experiment leverages Google Vision API and embedding models to filter through images for objects of interest. Potential applications include accelerating the data labeling process, and swiftly sorting or archiving images.
    • Augmented Learning for Image Classification Improve image classification model performance in cases of insufficient data, and/or incomplete or imperfect labels. Bring your as-is training data and receive access to two CNN models: one trained with just your data, and a second with our augmentation learning technique.
    • Interpretable Image Classification with Prototypes We can better explain model predictions by surfacing the most similar items from training data. Customers bring their data, and we return a classifier that not only predicts the output, but also related examples (prototypes) that explain the decision.
    • Image Classification with Confidence Scores AI systems can be a lot more useful when we know how much we can rely on a certain prediction. This experiment generates an AI model that returns both the predicted class and a well-calibrated confidence score of the prediction.
    • Mixed Integer and Linear Program Solver This Cloud-based API solves mixed integer linear programs, which are systems of equations that optimize continuous and integer variables given a set of constraints. Google uses this solver every day for large-scale and business-critical optimization challenges. Applications include assignment, scheduling, packing, and flow problems.
    • Semantic Similarity for Natural Language For a given piece of text, find the most similar or related text items among a list of candidates. Built from correlations in natural language usage, this experiment helps to connect items based on meaning and usage rather than simple keywords.
    • Label Error Detector for Images With this experiment, customers bring their labeled training images, and we run it through a series of quality check tools. We return a list of training images that are potentially mislabeled.
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