The left, middle, and right sides represent standard convolution, deformable convolution, and T-deformable convolution respectively. The upper, middle, and lower sides represent the activation unit on the feature map, 3 ×3 filter, sampling position.</p
<p>Permutation-corrected t-maps (left panel) and topographical maps (right panel) used to compare th...
Complexity comparison between the regular convolution kernel versus the depthwise separable convolut...
Difference (arbitrary unit) between denoised image and original noisy image (top) and the ground tru...
Deformable convolutional sampling points adaptive offset. (a) Standard convolution sampling point; (...
Both operations use a 3x3 kernel and a stride of two. Traditional convolution determines the output ...
The left side is the feature extraction branch, and the right side is the offset learning branch. Th...
A dilated temporal convolution with factor d = 1, 2, 4 and filter size k = 3.</p
The map of the difference between the weighted sums of the spatial filters in the “Mix” model and th...
In all examples the kernel size is 3x3, but the rate differs. The rate defines by which factor the f...
(a) Single Scale Regular Convolution (b), Multi-Scale Regular Convolution, and (c) Multi-Scale Depth...
Extraction of characteristics: Location of neighboring pixels in red (on the left); multi-resolution...
<p>Contour maps of different ear samples (the top row is contour maps drawn from one ear, the bottom...
Comparison of accuracy and feature dimension under different methods based on RFECV.</p
A dilated casual convolution with dilated factors d = 1,2,4 and filter kernel size k = 3.</p
<p>Each sampled region is concatenated with dividing columns (zeros) and displayed in dynamic contra...
<p>Permutation-corrected t-maps (left panel) and topographical maps (right panel) used to compare th...
Complexity comparison between the regular convolution kernel versus the depthwise separable convolut...
Difference (arbitrary unit) between denoised image and original noisy image (top) and the ground tru...
Deformable convolutional sampling points adaptive offset. (a) Standard convolution sampling point; (...
Both operations use a 3x3 kernel and a stride of two. Traditional convolution determines the output ...
The left side is the feature extraction branch, and the right side is the offset learning branch. Th...
A dilated temporal convolution with factor d = 1, 2, 4 and filter size k = 3.</p
The map of the difference between the weighted sums of the spatial filters in the “Mix” model and th...
In all examples the kernel size is 3x3, but the rate differs. The rate defines by which factor the f...
(a) Single Scale Regular Convolution (b), Multi-Scale Regular Convolution, and (c) Multi-Scale Depth...
Extraction of characteristics: Location of neighboring pixels in red (on the left); multi-resolution...
<p>Contour maps of different ear samples (the top row is contour maps drawn from one ear, the bottom...
Comparison of accuracy and feature dimension under different methods based on RFECV.</p
A dilated casual convolution with dilated factors d = 1,2,4 and filter kernel size k = 3.</p
<p>Each sampled region is concatenated with dividing columns (zeros) and displayed in dynamic contra...
<p>Permutation-corrected t-maps (left panel) and topographical maps (right panel) used to compare th...
Complexity comparison between the regular convolution kernel versus the depthwise separable convolut...
Difference (arbitrary unit) between denoised image and original noisy image (top) and the ground tru...