Convolution operations #11

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julius · 2024-01-03T14:38:57Z

julius commented

2024-01-03 14:38:57 +00:00

Convolution operations are fundamental in the field of signal processing and have become especially crucial in deep learning, particularly in the context of Convolutional Neural Networks (CNNs). These operations involve applying a filter or kernel to an input tensor (like an image) to extract features.

Standard Convolution:
- Applying a filter across the input tensor to produce a feature map.
- Typically involves parameters like kernel size, stride, and padding.
Dilated Convolution:
- A convolution operation with defined gaps, used for expanding the receptive field without increasing the number of parameters.
Transposed Convolution (Deconvolution):
- Used for upsampling an input tensor, common in tasks like semantic segmentation.
- Often involves reversing the forward and backward passes of a standard convolution.
Depthwise Convolution:
- Each input channel is convolved with its own kernel, reducing computation compared to standard convolution.
- Common in mobile and efficient architectures like MobileNets.
Pointwise Convolution (1x1 Convolution):
- Involves a 1x1 kernel, used mainly to change the dimensionality (depth) of the feature map.
- Efficient for computational cost and parameter reduction.
Separable Convolution (Depthwise Separable Convolution):
- A combination of depthwise and pointwise convolutions.
- Reduces the number of parameters and computations.
Grouped Convolution:
- Divides the input channels into groups and performs convolutions within each group separately.
- Useful for parallel processing and reducing computational load.
Atrous Convolution (same as Dilated Convolution):
- Includes gaps in the convolution process to capture larger contexts without increasing kernel size.
3D Convolution:
- Used for volumetric data (like video or medical imaging), where the convolution is applied in three dimensions.

Convolution operations are fundamental in the field of signal processing and have become especially crucial in deep learning, particularly in the context of Convolutional Neural Networks (CNNs). These operations involve applying a filter or kernel to an input tensor (like an image) to extract features. 1. **Standard Convolution**: - [ ] Applying a filter across the input tensor to produce a feature map. - [ ] Typically involves parameters like kernel size, stride, and padding. 2. **Dilated Convolution**: - [ ] A convolution operation with defined gaps, used for expanding the receptive field without increasing the number of parameters. 3. **Transposed Convolution (Deconvolution)**: - [ ] Used for upsampling an input tensor, common in tasks like semantic segmentation. - [ ] Often involves reversing the forward and backward passes of a standard convolution. 4. **Depthwise Convolution**: - [ ] Each input channel is convolved with its own kernel, reducing computation compared to standard convolution. - [ ] Common in mobile and efficient architectures like MobileNets. 5. **Pointwise Convolution (1x1 Convolution)**: - [ ] Involves a 1x1 kernel, used mainly to change the dimensionality (depth) of the feature map. - [ ] Efficient for computational cost and parameter reduction. 6. **Separable Convolution (Depthwise Separable Convolution)**: - [ ] A combination of depthwise and pointwise convolutions. - [ ] Reduces the number of parameters and computations. 7. **Grouped Convolution**: - [ ] Divides the input channels into groups and performs convolutions within each group separately. - [ ] Useful for parallel processing and reducing computational load. 8. **Atrous Convolution (same as Dilated Convolution)**: - [ ] Includes gaps in the convolution process to capture larger contexts without increasing kernel size. 9. **3D Convolution**: - [ ] Used for volumetric data (like video or medical imaging), where the convolution is applied in three dimensions.

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julius referenced this issue

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julius added this to the manifold project 2024-01-06 00:28:35 +00:00

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