Sequence Consistency Evaluation (SCE) tests

Naive Few-Shot Learning: Uncovering the fluid intelligence of machines

This paper shows that naive (randomly initialized) deep learning models that are trained on a single SCE with a single optimization step can still solve non-trivial versions of the task relatively well, and extends the findings to solve, without any prior training, real-world anomaly detection tasks in the visual and auditory modalities.