1. Introduction

2. Methods  
2.1. Deep AstroUDA Method  
2.2. Adaptive Clustering Loss  
2.3. Entropy Separation Loss  
2.4. Total Loss and Model Training  
2.5. Hyperparameter Tuner  
2.6. Latent Space Visualization  

3. Neural Network Model and Training

4. Data  
4.1. Simulations - LSST Mock Data  
4.2. Observations  
4.3. Data Pre-Processing  

5. Results  
5.2. Domain adaptation across surveys: SDSS to DECaLS  
5.3. Domain adaptation within a survey: wide and deep fields of SDSS  
5.4. Comparison to other methods and benchmarks  

6. Discussion and Conclusion