fashion mnist
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Appendix 465 Table of Contents
The Appendix is organized as follows.
Technology:
eddea82ad2755b24c4e168c5fc2ebd40-Supplemental.pdf
The background model is trained byperturbing aproportion ofrandomly chosen pixels,wheretheperturbation isdonebyreplacing the pixel value by a uniformly sampled random value between 0 and 255.
Interactive Label Cleaning with Example-based Explanations
The number of cleaned counter-examples across data sets and models is more than 30% of the total number of cleaned examples. FIM-based approaches outperform the LISSA estimator. FIM, which is difficult to store and invert. Figure 3 shows the results of the evaluation of Top Fisher, Practical Fisher and nearest neighbor (NN). As reported in the main text, Practical Fisher lags behind Top Fisher in all cases.
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