2 BACKGROUND

2.1 Random Forests Revisited

2.2 Deep Convolutional Neural Networks

3 DEEP TRANSIENT DETECTION

3.1 Imaging Data

3.2 Baseline: Random Forests & Features

3.3 Network Structures & Parameters

4 ANALYSIS

4.1 Experimental Setup

4.2 Results

5 CONCLUSIONS AND OUTLOOK