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Аллахвердов Эдуард
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README.md

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## Orthogonal Transformation Learning
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## Orthogonal Transformation Learning For R and B channels Swap
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To study orthogonal transformations in feature space:
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1. Generate dataset for `google/siglip2-base-patch16-512`
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</figure>
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## Linear Transformation Learning For B Channel Suppression
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To study linear transformations in feature space:
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1. Generate dataset for `google/siglip2-base-patch16-512`
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2. Train reconstructor or use precomputed [weights](https://drive.google.com/file/d/1i-B-5yBpSwcZL3_Z2Dz53jfxiY9T-fkb/view?usp=drive_link)
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3. Place weights at:
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```bash
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metrics_calculation/precalculated_weights/models--google--siglip2-base-patch16-512.pt
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```
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4. Run the analysis notebook:
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```
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metrics_calculation/b_channel_suppression/understanding_b_suppression.ipynb
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```
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Example output:
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<div align="center">
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<figure>
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<img src="resources/b_suppression_all_transformations.png" width="600">
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<div>
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<figcaption>B Channel Suppression in Feature Space</figcaption>
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</div>
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</figure>
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</div>
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## Linear Transformation Learning For Colorization
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To study linear transformations in feature space:
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1. Generate dataset for `google/siglip2-base-patch16-512`
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2. Train reconstructor or use precomputed [weights](https://drive.google.com/file/d/1i-B-5yBpSwcZL3_Z2Dz53jfxiY9T-fkb/view?usp=drive_link)
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3. Place weights at:
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```bash
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metrics_calculation/precalculated_weights/models--google--siglip2-base-patch16-512.pt
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```
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4. Run the analysis notebook:
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```
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metrics_calculation/colorization/understanding_colorization.ipynb
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```
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Example output:
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<div align="center">
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<figure>
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<img src="resources/colorized_all_examples.png" width="600">
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<div>
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<figcaption>Colorization in Feature Space</figcaption>
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</div>
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</figure>
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</div>
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resources/colorized_all_examples.png

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