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Because the test categories are not present during training, this dataset provides a useful benchmark for evaluating whether reconstruction models can generalize beyond the training categories. A small set of non-naturalistic abstract stimuli (e.g., crosses) from this data has been used widely and successfully to demonstrate reconstruction capability outside the (naturalistic) training distributions.

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Transform backpressure gaps: Pull-through transforms execute on-demand. Data doesn't cascade through intermediate buffers; it flows only when the consumer pulls. Stop iterating, stop processing.

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