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\"PSNR of low resolution image and high resolution image is %.4f\" % bicubic_psnr |
) |
print(\"PSNR of predict and high resolution is %.4f\" % test_psnr) |
plot_results(lowres_img, index, \"lowres\") |
plot_results(highres_img, index, \"highres\") |
plot_results(prediction, index, \"prediction\") |
print(\"Avg. PSNR of lowres images is %.4f\" % (total_bicubic_psnr / 10)) |
print(\"Avg. PSNR of reconstructions is %.4f\" % (total_test_psnr / 10)) |
PSNR of low resolution image and high resolution image is 28.2682 |
PSNR of predict and high resolution is 29.7881 |
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PSNR of low resolution image and high resolution image is 23.0465 |
PSNR of predict and high resolution is 25.1304 |
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PSNR of low resolution image and high resolution image is 25.4113 |
PSNR of predict and high resolution is 27.3936 |
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PSNR of low resolution image and high resolution image is 26.5175 |
PSNR of predict and high resolution is 27.1014 |
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PSNR of low resolution image and high resolution image is 24.2559 |
PSNR of predict and high resolution is 25.7635 |
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PSNR of low resolution image and high resolution image is 23.9661 |
PSNR of predict and high resolution is 25.9522 |
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PSNR of low resolution image and high resolution image is 24.3061 |
PSNR of predict and high resolution is 26.3963 |
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PSNR of low resolution image and high resolution image is 21.7309 |
PSNR of predict and high resolution is 23.8342 |
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PSNR of low resolution image and high resolution image is 28.8549 |
PSNR of predict and high resolution is 29.6143 |
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PSNR of low resolution image and high resolution image is 23.9198 |
PSNR of predict and high resolution is 25.2592 |
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Avg. PSNR of lowres images is 25.0277 |
Avg. PSNR of reconstructions is 26.6233 |
Deep dive into location-specific and channel-agnostic involution kernels. |
Introduction |
Convolution has been the basis of most modern neural networks for computer vision. A convolution kernel is spatial-agnostic and channel-specific. Because of this, it isn't able to adapt to different visual patterns with respect to different spatial locations. Along with location-related problems, the receptive field of... |
To address the above issues, Li et. al. rethink the properties of convolution in Involution: Inverting the Inherence of Convolution for VisualRecognition. The authors propose the \"involution kernel\", that is location-specific and channel-agnostic. Due to the location-specific nature of the operation, the authors say ... |
This example describes the involution kernel, compares two image classification models, one with convolution and the other with involution, and also tries drawing a parallel with the self-attention layer. |
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