Are Vision Transformers More Data Hungry Than Newborn Visual Systems?
This addresses the problem of understanding biological learning mechanisms for AI researchers and neuroscientists, showing that ViTs can mimic animal-like learning under constrained conditions, though it is incremental in bridging AI and neuroscience.
The study tested whether vision transformers (ViTs) are more data-hungry than biological visual systems by comparing them to newborn chicks in impoverished visual environments, finding that ViTs trained with simulated chick data achieved similar view-invariant object recognition without requiring more data.
Vision transformers (ViTs) are top performing models on many computer vision benchmarks and can accurately predict human behavior on object recognition tasks. However, researchers question the value of using ViTs as models of biological learning because ViTs are thought to be more data hungry than brains, with ViTs requiring more training data to reach similar levels of performance. To test this assumption, we directly compared the learning abilities of ViTs and animals, by performing parallel controlled rearing experiments on ViTs and newborn chicks. We first raised chicks in impoverished visual environments containing a single object, then simulated the training data available in those environments by building virtual animal chambers in a video game engine. We recorded the first-person images acquired by agents moving through the virtual chambers and used those images to train self supervised ViTs that leverage time as a teaching signal, akin to biological visual systems. When ViTs were trained through the eyes of newborn chicks, the ViTs solved the same view invariant object recognition tasks as the chicks. Thus, ViTs were not more data hungry than newborn visual systems: both learned view invariant object representations in impoverished visual environments. The flexible and generic attention based learning mechanism in ViTs combined with the embodied data streams available to newborn animals appears sufficient to drive the development of animal-like object recognition.