# CactusNets: Layer Applicability as a Metric for Transfer Learning

arXiv: https://arxiv.org/abs/1804.07846 · HTML: https://mlsanity.com/paper/1804.07846/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [COMBOOD: A Semiparametric Approach for Detecting Out-of-distribution Data for Image Classification](https://mlsanity.com/paper/2602.07042/) (2026)
- [Benchmarking Artificial Intelligence Models for Daily Coastal Hypoxia Forecasting](https://mlsanity.com/paper/2602.05178/) (2026)

## Abstract (arXiv)

Deep neural networks trained over large datasets learn features that are both generic to the whole dataset, and specific to individual classes in the dataset. Learned features tend towards generic in the lower layers and specific in the higher layers of a network. Methods like fine-tuning are made possible because of the ability for one filter to apply to multiple target classes. Much like the human brain this behavior, can also be used to cluster and separate classes. However, to the best of our knowledge there is no metric for how applicable learned features are to specific classes. In this paper we propose a definition and metric for measuring the applicability of learned features to individual classes, and use this applicability metric to estimate input applicability and produce a new method of unsupervised learning we call the CactusNet.