ALGORITHM II.

The Main Procedure of our method (gda)

Input: $X_s$, $X_t$, $Y$

Initialization: $\mathit{\text{iteration}}=250$, $\mathit{\text{batch\hspace{0.17em}size }}=8$, $\mathit{\text{learning\hspace{0.17em}rate}}=\text{0.01}$, $\mathit{\text{momentum}}=0.9$

Output: $\mathit{\text{Los}}{s}_{\mathit{\text{total}}}$

For $i=1:\mathit{\text{iteration}}$ do

1. Sample $n$ images from source domain $X_s$

2. Sample $n$ images from target domain $X_t$

3. Take source and target samples into general subspace

4. Take source and target general features into domain-specific subspaces

5. Take source domain-specific features into source domain-specific discriminator

6. Use source and target domain-specific features to calculate low-rank loss

7. Weigh the source classifier loss by considering low-rank loss

8. Use target domain-specific features to calculate target discriminator loss

9. Update total loss

end for