1 INTRODUCTION

2 CONCEPTS AND PRELIMINARIES

3 DATA AUGMENTATION TO EVALUATE THE TRUE DATA DISTRIBUTION WITH MAXIMUM PROBABILITY

4 TRANSFER LEARNING BUILDS DATA-TO-LABEL MAPPINGS FOR SPECIFIC PROBLEMS

5 META-LEARNING DERIVES TASK-TO-TARGET MODEL MAPPINGS INDEPENDENT OF SPECIFIC PROBLEMS

6 MULTIMODAL COMPLEMENTARY LEARNING OF SMALL SAMPLES WITH LIMITED INFORMATION

7 FSL APPLICATIONS IN COMPUTER VISION

8 FUTURE DIRECTION AND OPPORTUNITIES OF FSL

9 CONCLUSION