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# Deep Residual Learning for Image Recognition

## Abstract

Deeper neural networks are more difficult to train. We present a residual learning framework...

## 1. Introduction

Deep networks naturally integrate low/mid/high-level features [#bib:1] and classifiers in an end-to-end multi-layer fashion...

### 1.1. Residual Learning

Instead of hoping each few stacked layers directly fit a desired underlying mapping $\mathcal{H}(x)$, we explicitly let these layers fit a residual mapping:

$$\mathcal{F}(x) := \mathcal{H}(x) - x$$

The original mapping is recast into $\mathcal{F}(x) + x$...