CPC G06N 3/08 (2013.01) [G06N 3/045 (2023.01); G06N 20/10 (2019.01)] | 20 Claims |
1. A method comprising:
obtaining a neural network having a plurality of layers that was trained to perform a first inference task;
labeling individual samples in an input dataset for the neural network with class labels for a second inference task to assign the individual samples to classes corresponding to the second inference task;
applying the neural network to the input dataset to obtain intermediate feature vectors for the first inference task from an intermediate layer of the plurality of layers;
clustering the intermediate feature vectors into a plurality of clusters;
comparing the clusters of intermediate feature vectors for the first inference task with the classes of samples labeled according to the class labels for the second inference task to determine a coherence score of the intermediate layer, wherein the coherence score indicates a semantic coherence of features produced by the intermediate layer for performing the second inference task; and
outputting the coherence score for the intermediate layer.
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