---
title: "Deep Residual Learning for Image Recognition"
description: "ResNet is the skip-connection paper that makes depth trainable in practice. It is beginner-friendly because the core idea is simple: learn a residual change instead of relearning the whole representation."
canonical_url: "https://fanout.sh/daily/2026-07-09-resnet"
md_url: "https://fanout.sh/daily/2026-07-09-resnet.md"
last_updated: "2026-07-09"
access: "public"
---

# Deep Residual Learning for Image Recognition

ResNet is the skip-connection paper that makes depth trainable in practice. It is beginner-friendly because the core idea is simple: learn a residual change instead of relearning the whole representation.

## Paper details

- Authors: He et al.

- Venue: CVPR 2016

- Track: AI Research

- Difficulty: Very Easy

- Reading time: 35 min

- Original paper: https://arxiv.org/abs/1512.03385

## What you will learn

- Why very deep networks can degrade even before overfitting is the issue.

- How identity shortcuts improve gradient flow and optimization.

- Why residual thinking reappears in modern Transformer blocks.

## Continue exploring

[Explore CNN Architectures](https://fanout.sh/knowledge-graph?node=ai-research%3Acategory%3Aglossary-architecture&detail=ai-research%3Aconcept%3Acnn): Use Fanout's architecture glossary to connect ResNet to CNNs, residual blocks, and deeper model design.

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