v0.1.0: Initial Release

What's Changed

• feat: add abstract base class for approaches by @tilman151 in #1
• fix: remove manual feature extractor by @tilman151 in #3
• feat: add feature extractor models by @tilman151 in #2
• feat: add head model by @tilman151 in #4
• feat: add losses by @tilman151 in #5
• feat: switch adaption losses over to torchmetrics by @tilman151 in #7
• fix: put dropout layers in right positions for rnns by @tilman151 in #8
• feat: make set_model open for override by @tilman151 in #9
• feat: create domain labels inside dann loss by @tilman151 in #10
• fix: rul score backward by @tilman151 in #11
• fix: make adaption metrics movable between devices by @tilman151 in #12
• fix: diverging devices in adaption metrics by @tilman151 in #13
• feat: add dropout option to fc head by @tilman151 in #15
• fix: remove dropout from cnn input by @tilman151 in #16
• fix: remove cpu only pytorch by @tilman151 in #18
• feat: enable checkpointing by @tilman151 in #19
• feat: add gradient weight to GRL by @tilman151 in #20
• fix: introduce epsilon for pairwise euclidean by @tilman151 in #22
• feat: add lstm dann by @tilman151 in #14
• feat: add consistency dann by @tilman151 in #17
• feat: add adarul by @tilman151 in #21
• feat: add tbigru by @tilman151 in #23
• feat: add latent alignment approach by @tilman151 in #6
• docs: update intro and readme by @tilman151 in #24

New Contributors

• @tilman151 made their first contribution in #1

Full Changelog: https://github.com/tilman151/rul-adapt/commits/0.1.0