1 INTRODUCTION

2 DEEP LENS SURVEY DATA
2.1 Source selection and regions of interest

3 DEEP LEARNING ARCHITECTURE AND LEARNING METHODS USED
3.1 Convolutional neural network architecture
3.2 Domain adaptation with semi-supervised learning
3.3 Data augmentation and GANs

4 TRAINING AND VALIDATION DATA
4.1 Generating the NonLenses dataset
4.2 Generating the simulated Lenses dataset
4.3 Generating the training datasets: TrainingV1 and TrainingV2

5 METRIC TO EVALUATE MODEL PERFORMANCE
5.1 Generating the Testing dataset
5.2 Precision and Recall

6 RESULTS AND DISCUSSION
6.1 Semi-supervised algorithms with GANs and Augmentations have superior performance
6.2 Catalog of Lens candidates found
6.3 Lensing signatures identified by the models
6.4 Implications for future large-area sky surveys: sensitivity and angular resolution

7 CONCLUSIONS