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ver 0.3.3 stable inner product
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smilesun committed Jan 7, 2024
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7 changes: 5 additions & 2 deletions README.md
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Expand Up @@ -63,10 +63,13 @@ See example here: [Transformer as feature extractor, decorate JIGEN with DANN, t

### Benchmark different methods
DomainLab provides a powerful benchmark functionality.
To benchmark several algorithms(combination of neural networks, models, trainers and associated hyperparameters), a single line command along with a benchmark configuration files is sufficient. See details in [Benchmarks](./docs/doc_benchmark.md)
To benchmark several algorithms(combination of neural networks, models, trainers and associated hyperparameters), a single line command along with a benchmark configuration files is sufficient. See details in [benchmarks documentation and tutorial](./docs/doc_benchmark.md)

One could simply run
`bash run_benchmark_slurm.sh your_benchmark_configuration.yaml` to launch different experiments with specified configuraiton. For example, the following result (without any augmentation like flip) is for PACS dataset.
`bash run_benchmark_slurm.sh your_benchmark_configuration.yaml` to launch different experiments with specified configuraiton.


For example, the following result (without any augmentation like flip) is for PACS dataset.

<div style="align: center; text-align:center;">
<img src="https://github.com/marrlab/DomainLab/blob/master/docs/figs/stochastic_variation_two_rows.png" style="width:800px;"/>
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6 changes: 3 additions & 3 deletions docs/conf.py
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Expand Up @@ -181,7 +181,7 @@
{
"href": "doc_benchmark",
"internal": True,
"title": "Benchmarks"},
"title": "Benchmarks tutorial"},

{
"href": "doc_output",
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# },
],
"heroes": {
"index": "DomainLab for modular domain generalization",
"index": "DomainLab for modular domain generalization in deep learning",
"customization": "Configuration options to personalize your site.",
},
"version_dropdown": False,
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"DomainLab Documentation",
author,
"domainlab",
"A pytorch platform for domain generalization",
"A pytorch platform of modular domain generalization for deep learning",
"Miscellaneous",
),
]
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4 changes: 4 additions & 0 deletions docs/doc_mldg.md
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# Trainer MLDG
## Meta Learning Domain Generalization

Li, Da, et al. "Learning to generalize: Meta-learning for domain generalization." Proceedings of the AAAI conference on artificial intelligence. Vol. 32. No. 1. 2018.
1 change: 1 addition & 0 deletions docs/index.rst
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Expand Up @@ -18,6 +18,7 @@ Welcome to domainlab's documentation!
docMatchDG
docDIAL
docFishr
doc_mldg


Indices and tables
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2 changes: 1 addition & 1 deletion pyproject.toml
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@@ -1,6 +1,6 @@
[tool.poetry]
name = "domainlab"
version = "0.3.2"
version = "0.3.3"
readme = "README.md"
repository = "https://github.com/marrlab/DomainLab"
homepage = "https://marrlab.github.io/DomainLab/"
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -35,7 +35,7 @@ def copy_dir(dir_path="zdata"):
package_data = {
'zdata': [f for f in copy_dir()],
},
version='0.3.2',
version='0.3.3',
description='Library of modular domain generalization for deep learning',
url='https://github.com/marrlab/DomainLab',
author='Xudong Sun, et.al.',
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