Understanding Convolutional Operators in Neural Networks

1. Background of Convolution

2. Convolution Kernel / Feature Map / Convolution Calculation

3. Padding

4. Stride

5. Receptive Field

6. Multiple Input Channels, Multiple Output Channels, and Batch Operations

7. Advantages of Convolution

8. Example Applications of Convolution

References

1. 1×1 Convolution

2. Example Applications

References

1. 3D Convolution

2. Example Applications

References

1. Background of Transposed Convolution

2. Transposed Convolution and its Applications

3. Differences Between Transposed Convolution and Standard Convolution

4. Mathematical Derivation of Transposed Convolution

5. Output Feature Map Size of Transposed Convolution

1. Background of Dilated Convolution

2. Dilated Convolution and its Applications

3. Differences Between Dilated Convolution and Standard Convolution

4. Receptive Field of Dilated Convolution

1. Background of Grouped Convolution

2. Differences Between Grouped Convolution and Standard Convolution

3. Example Applications

1. Background of Separable Convolution

2. Spatial Separable Convolution

3. Depthwise Separable Convolution

1. Background of Deformable Convolution

2. Deformable Convolution

3. DCN v2