# CNN 101: Interactive Visual Learning for Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2001.02004 · HTML: https://mlsanity.com/paper/2001.02004/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2020.

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

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- [Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations](https://mlsanity.com/paper/2307.04036/) (2023)
- [A class-driven hierarchical ResNet for classification of multispectral remote sensing images](https://mlsanity.com/paper/2510.08060/) (2025)

## Abstract (arXiv)

The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology. However, it is often challenging for learners to take the first steps due to the complexity of deep learning models. We present our ongoing work, CNN 101, an interactive visualization system for explaining and teaching convolutional neural networks. Through tightly integrated interactive views, CNN 101 offers both overview and detailed descriptions of how a model works. Built using modern web technologies, CNN 101 runs locally in users' web browsers without requiring specialized hardware, broadening the public's education access to modern deep learning techniques.