I. INTRODUCTION  
II. RELATED WORK  
III. DATA AUGMENTATION  
A. STRONG AUGMENTATION  
B. WEAK AUGMENTATION  
C. HYBRID AUGMENTATION  
IV. SEMI-SUPERVISED STRATEGIES  
A. PSEUDO LABELS  
B. CONSISTENT REGULARIZATION  
C. GRAPH-BASED  
D. TRANSFER LEARNING-BASED  
V. CHALLENGING SETTING  
VI. LOSS FUNCTION  
A. SMOOTH L1 LOSS  
B. FOCAL LOSS  
C. SIMILARITY LEARNING LOSS  
D. DISTILLATION LOSS  
E. KL DIVERGENCE  
F. QUALITY FOCAL LOSS  
G. JENSEN-SHANNON DIVERGENCE  
VII. DATASETS AND COMPARISON RESULTS  
VIII. CONCLUSION AND FUTURE DIRECTIONS