Partial Domain Adaptation (PDA) is a domain adaptation scenario where the target domain's label space is a subset of the source domain's label space
Class Conditional Alignment (CCA-PDA)
CCA - is a well-designed method for partial domain adaptation that directly tackles the class mismatch issue between source and target domains.
It uses a multi-class adversarial loss to perform this alignment, ensuring that only the shared classes between source and target are emphasized. This helps avoid negative transfer from source-only classes.
Datasets
Caltech as the source and Office-31 as the target is a classic partial domain adaptation (PDA) scenario, since Caltech has a broader label space (256 classes) while Office-31 has only 31. This means you’ll need to filter out the irrelevant Caltech classes to avoid negative transfer.
upload caltech source from kaggle
During the Class Conditional Alignment (CCA-PDA) process, discriminators are trained per class to distinguish source vs. target domain features. The results below indicate which classes successfully received target samples and which were skipped due to insufficient data.
| Class Index | Source Samples | Target Samples | Status | Loss Value |
|---|---|---|---|---|
| 0 | 30,607 | 1,106 | ✅ Trained | 0.6472 |
| 1 | 30,607 | 1,711 | ✅ Trained | 0.6960 |
| 2 | 0 | 0 | N/A | |
| 3 | 0 | 0 | N/A | |
| 4 | 0 | 0 | N/A | |
| 5 | 0 | 0 | N/A | |
| 6 | 0 | 0 | N/A | |
| 7 | 0 | 0 | N/A | |
| 8 | 0 | 0 | N/A | |
| 9 | 0 | 0 | N/A |
- Classes 0 & 1 trained successfully, meaning the model was able to extract enough target samples to align them.
- Classes 2–9 were skipped due to zero source or target samples, implying either dataset mismatch, category absence, or confidence filtering.
- Lowering the confidence threshold (currently
0.7) may increase target samples for more classes.
- Verify class overlap between the source (Caltech-10) and target (Office-31) domains.
- Visualize target feature distributions to confirm if missing classes exist but lack high-confidence predictions.
- Adjust pseudo-labeling strategies to better populate underrepresented classes.
Possible Causes:
Classifier bias toward a few classes
Your classifier might be overfitting to dominant classes in Caltech-10, ignoring others when assigning pseudo-labels.
Some classes may have lower feature separability, making the softmax outputs less confident.
Target domain shift
Domain shift causes category mismatch—even if some objects in Office-31 belong to a "shared" class, the visual domain is different enough that your classifier might not recognize them confidently.
Confidence threshold cutting too aggressively
If your threshold (confidence_threshold = 0.7) is too high, most target samples won’t qualify as confidently pseudo-labeled.
Lowering it to 0.5 or even 0.3 might help populate more classes.