# Object Detection, Recognition, Deep Learning, and the Universal Law of Generalization

Source: [Object Detection, Recognition, Deep Learning, and the Universal Law of Generalization](<https://www.sota2.com/research/paper/68fa6cc21e209131cea35d99>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:18:38.162Z

Authors: Faris B. Rustom, Haluk \\"O\\u{g}men, Arash Yazdanbakhsh

Published: 2022-06-10T22:26:29.000Z

## Abstract

Object detection and recognition are fundamental functions underlying the success of species. Because the appearance of an object exhibits a large variability, the brain has to group these different stimuli under the same object identity, a process of generalization. Does the process of generalization follow some general principles or is it an ad-hoc "bag-of-tricks"? The Universal Law of Generalization provided evidence that generalization follows similar properties across a variety of species and tasks. Here we test the hypothesis that the internal representations underlying generalization reflect the natural properties of object detection and recognition in our environment rather than the specifics of the system solving these problems. By training a deep-neural-network with images of "clear" and "camouflaged" animals, we found that with a proper choice of category prototypes, the generalization functions are monotone decreasing, similar to the generalization functions of biological systems. Our findings support the hypothesis of the study.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2206.05365>)

[PDF](<https://arxiv.org/pdf/2206.05365.pdf>)