What do larger image classifiers memorise?
Michal Lukasik Vaishnavh Nagarajan Ankit Singh Rawat Aditya Krishna Menon Sanjiv Kumar
The success of modern neural networks has prompted study of the connection
between memorisation and generalisation: overparameterised models generalise
well, despite being able to perfectly fit (memorise) completely random labels.
To carefully study this issue, Feldman proposed a metric to quantify the degree
of memorisation of individual training examples, and empirically computed the
corresponding memorisation profile of a ResNet on image classification
bench-marks. While an exciting first glimpse into what real-world models
memorise, this leaves open a fundamental question: do larger neural models
memorise more? We present a comprehensive empirical analysis of this question
on image classification benchmarks. We find that training examples exhibit an
unexpectedly diverse set of memorisation trajectories across model sizes: most
samples experience decreased memorisation under larger models, while the rest
exhibit cap-shaped or increasing memorisation. We show that various proxies for
the Feldman memorization score fail to capture these fundamental trends.
Lastly, we find that knowledge distillation, an effective and popular model
compression technique, tends to inhibit memorisation, while also improving
generalisation. Specifically, memorisation is mostly inhibited on examples with
increasing memorisation trajectories, thus pointing at how distillation
improves generalisation.