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ACCURATELY IDENTIFYING MEMBERS OF TRAINING DATA IN VARIATIONAL AUTOENCODERS BY RECONSTRUCTION ERROR
ACCURATELY IDENTIFYING MEMBERS OF TRAINING DATA IN VARIATIONAL AUTOENCODERS BY RECONSTRUCTION ERROR
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机译:通过重构错误准确识别各种自动编码器中的训练数据成员
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摘要
A system is described that can include a machine learning model and at least one programmable processor communicatively coupled to the machine learning model. The machine learning model can receive data, generate a continuous probability distribution associated with the data, sample a latent variable from the continuous probability distribution to generate a plurality of samples, and generate reconstructed data from the plurality of samples. The at least one programmable processor can compute a reconstruction error by determining a distance between the reconstructed data and the data, and generate, based on the reconstruction error, an indication representing whether a specific record within the received data was used to train the machine learning model. Related apparatuses, methods, techniques, non-transitory computer programmable products, non-transitory machine-readable medium, articles, and other systems are also within the scope of this disclosure.
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