# CINIC-10 is not ImageNet or CIFAR-10

arXiv: https://arxiv.org/abs/1810.03505 · HTML: https://mlsanity.com/paper/1810.03505/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Energy-based Automated Model Evaluation](https://mlsanity.com/paper/2401.12689/) (2024)
- [Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions](https://mlsanity.com/paper/2401.04305/) (2024)
- [A New Federated Learning Framework Against Gradient Inversion Attacks](https://mlsanity.com/paper/2412.07187/) (2024)
- [fluke: Federated Learning Utility frameworK for Experimentation and research](https://mlsanity.com/paper/2412.15728/) (2024)
- [Lightweight Adaptive ReduNet via Hyperspherical Manifold Learning](https://mlsanity.com/paper/2608.20668/) (2026)
- [Resilience Beyond Stationary Client Unavailability: Unlocking Efficient and Unbiased Federated Learning](https://mlsanity.com/paper/2609.04763/) (2026)
- [Power Side-Channel Membership Inference Attack on Embedded Machine Learning](https://mlsanity.com/paper/2610.10909/) (2026)

## Abstract (arXiv)

In this brief technical report we introduce the CINIC-10 dataset as a plug-in extended alternative for CIFAR-10. It was compiled by combining CIFAR-10 with images selected and downsampled from the ImageNet database. We present the approach to compiling the dataset, illustrate the example images for different classes, give pixel distributions for each part of the repository, and give some standard benchmarks for well known models. Details for download, usage, and compilation can be found in the associated github repository.