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Found 2 papers, 1 papers with code
Manifold-based Test Generation for Image Classifiers
Our approach is based on the premise that patterns in a large input data space can be effectively captured in a smaller manifold space, from which similar yet novel test cases---both the input and the label---can be sampled and generated.
Input Prioritization for Testing Neural Networks
label, for a given input---is high, which significantly impacts the amount and quality of testing that can be performed.
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