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Showing 1 to 4 of 4 for “"Clean labels"”.

  1. Learning to classify images without explicit human annotations

    … first collecting examples along with candidate labels, second obtaining clean labels from workers, and third training a large, overparameterized deep neural network on the clean examples. The second, manual labeling step is often the most expensive one as it requires manually going through all …

    rice Repository record for Learning to classify images without explicit human annotations (opens in a new tab)

  2. Scalable Robust Models Under Adversarial Data Corruption

    … self-paced robust learning for leveraging clean labels in noisy data, and robust regression via online feature selection with adversarial noises. Moreover, I extended the self-paced robust learning method to its distributed version for the scalability of the proposed algorithm, named …

    vt Repository record for Scalable Robust Models Under Adversarial Data Corruption (opens in a new tab)

  3. Inferring Shape and Material from Sound

    … containing sounds of various objects, with clean labels on their appearances, shape and material. However, it is difficult and expensive to capture such a dataset. Another approach is to tackle the problem in an analysis-by-synthesis framework, where we iterative update current estimates …

    mit Repository record for Inferring Shape and Material from Sound (opens in a new tab)

  4. An analysis of consumer perceptions, attitudes, visual attention allocation, and willingness to pay for clean label food items

    … natural, organic, and non-GMO. Thus, the clean label trend emerged. This study examined consumers’ perceptions, attitudes, willingness to pay, and visual attention allocation toward clean label food items. A standard definition for clean labels has yet to be developed; therefore, several …

    ttu Repository record for An analysis of consumer perceptions, attitudes, visual attention allocation, and willingness to pay for clean label food items (opens in a new tab)