@@ -109,7 +109,7 @@ Example output:
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</figure >
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</div >
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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 `
@@ -132,3 +132,52 @@ Example output:
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</div >
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</figure >
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</div >
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+
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+
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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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+
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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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+
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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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+
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+ ## Linear Transformation Learning For Colorization
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+ To study linear transformations in feature space:
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+
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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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+
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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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