Update README.md - Script Documentation

experiments/probabilistic_domain_adaptation/README.md

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+# Probabilistic Domain Adaption
+
+Implemention of [Probabilistic Domain Adaptation for Biomedical Image Segmentation](https://arxiv.org/abs/2303.11790) in `torch_em`.
+Please cite the paper if you are using these approaches in your research.
+
+## Self-Training Approaches
+
+The subfolders contain the training scripts for both separate and joint training setups:
+
+- `unet_source.py` (UNet Source Training): 
+```
+python unet_source.py -p [check / train / evaluate]
+                      -c <CELL-TYPE>
+                      -i <PATH-TO-DATA>
+                      -s <PATH-TO-SAVE-MODEL-WEIGHTS>
+                      -o <PATH-FOR-SAVING-PREDICTIONS>
+```
+
+- `unet_mean_teacher.py` (UNet Mean-Teacher Separate Training):
+```
+python unet_mean_teacher.py -p [check / train / evaluate]
+                            -c <CELL-TYPE>
+                            -i <PATH-TO-DATA>
+                            -s <PATH-TO-SAVE-MODEL-WEIGHTS>
+                            -o <PATH-FOR-SAVING-PREDICTIONS>
+                            [(optional) --confidence_threshold <THRESHOLD-FOR-COMPUTING-FILTER-MASK>]
+```
+
+- `unet_adamt.py` (UNet Mean-Teacher Joint Training):
+```
+python unet_adamt.py -p [check / train / evaluate]
+                     -c <CELL-TYPE>
+                     -i <PATH-TO-DATA>
+                     -s <PATH-TO-SAVE-MODEL-WEIGHTS>
+                     -o <PATH-FOR-SAVING-PREDICTIONS>
+                     [(optional) --confidence_threshold <THRESHOLD-FOR-COMPUTING-FILTER-MASK>]
+```
+
+- `unet_fixmatch.py` (UNet FixMatch Separate Training):
+```
+python unet_fixmatch.py -p [check / train / evaluate]
+                        -c <CELL-TYPE>
+                        -i <PATH-TO-DATA>
+                        -s <PATH-TO-SAVE-MODEL-WEIGHTS>
+                        -o <PATH-FOR-SAVING-PREDICTIONS>
+                        [(optional) --confidence_threshold <THRESHOLD-FOR-COMPUTING-FILTER-MASK>]
+                        [(optional) --distribution_alignment <ACTIVATES-DISTRIBUTION-ALIGNMENT>]
+```
+
+- `unet_adamatch.py` (UNet FixMatch Joint Training):
+```
+python unet_adamatch.py -p [check / train / evaluate]
+                        -c <CELL-TYPE>
+                        -i <PATH-TO-DATA>
+                        -s <PATH-TO-SAVE-MODEL-WEIGHTS>
+                        -o <PATH-FOR-SAVING-PREDICTIONS>
+                        [(optional) --confidence_threshold <THRESHOLD-FOR-COMPUTING-FILTER-MASK>]
+                        [(optional) --distribution_alignment <ACTIVATES-DISTRIBUTION-ALIGNMENT>]
+```