Figure 4:

Recovery of k-neighborhoods under feature corruption. Mean over 3 iterations is reported for each method. (a) Lazy classification accuracy relative to input size with unlabeled randomly corrupted digits with 35% preserved pixels. (b) Transfer learning performance. For each ratio, 1K uncorrupted, labeled digits were sampled from MNIST, and then 1K, 2K, 4K, and 8K (x-axis) unlabeled points were sampled and corrupted with 35% column identity.