Kevin Pedro, Gabriel Perdue, Brian Nord, Sandeep Madireddy, Aleksandra Ćiprijanović, Ashia Lewis, Stefan Wild
We lifted 2 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| deepskies/deepastrouda | canonical | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| flatten_list | Not yet run | deepskies/deepastrouda/deep_astro_uda/apex/RNN/RNNBackend.py code served (permissive licence) · get_code("aeb5cbf4e7c75b5f") |
| is_iterable | Not yet run | deepskies/deepastrouda/deep_astro_uda/apex/RNN/RNNBackend.py code served (permissive licence) · get_code("bb684c9e5a6606f6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other dataset. Here we develop a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain alignment that can be applied to datasets with different types of class overlap. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS). We show that our method is capable of bridging the gap between two astronomical surveys, and also performs well for anomaly detection and clustering of unknown data in the unlabeled dataset. We apply our model to two examples of galaxy morphology classification tasks with anomaly detection: 1) classifying spiral and elliptical galaxies with detection of merging galaxies (three classes including one unknown Preprint. Under review.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2211.00677")
get_code_for_paper("2211.00677")
have("2211.00677")
Connect an agent — have() is free.