# Breaking Inter-Layer Co-Adaptation by Classifier Anonymization

arXiv: https://arxiv.org/abs/1906.01150 · HTML: https://mlsanity.com/paper/1906.01150/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [NoT: Federated Unlearning via Weight Negation](https://mlsanity.com/paper/2503.05657/) (2025)
- [PoF: Post-Training of Feature Extractor for Improving Generalization](https://mlsanity.com/paper/2207.01847/) (2022)
- [Adaptive Adversarial Cross-Entropy Loss for Sharpness-Aware Minimization](https://mlsanity.com/paper/2406.14329/) (2024)
- [EMASAM: a Computationally Efficient Sharpness-Aware Minimization via EMA-Guided Perturbations](https://mlsanity.com/paper/2608.15105/) (2026)

## Abstract (arXiv)

This study addresses an issue of co-adaptation between a feature extractor and a classifier in a neural network. A naive joint optimization of a feature extractor and a classifier often brings situations in which an excessively complex feature distribution adapted to a very specific classifier degrades the test performance. We introduce a method called Feature-extractor Optimization through Classifier Anonymization (FOCA), which is designed to avoid an explicit co-adaptation between a feature extractor and a particular classifier by using many randomly-generated, weak classifiers during optimization. We put forth a mathematical proposition that states the FOCA features form a point-like distribution within the same class in a class-separable fashion under special conditions. Real-data experiments under more general conditions provide supportive evidences.