Convolutional Neural Networks

Adaptive pooling is a technique in deep learning that adjusts the size of output features to match specific requirements.

Atrous convolution is a type of convolution that uses dilated filters to capture multi-scale features in neural networks.

A convolutional layer is a key component in convolutional neural networks (CNNs) that processes and extracts features from input data.

Deformable Convolution enhances standard convolution by allowing flexible, learnable sampling locations.

Depthwise convolution is a type of convolutional layer that processes each input channel separately.

Depthwise Separable Convolution is an efficient convolution technique used in deep learning to reduce computational complexity.

Dilated convolution expands the filter's receptive field without increasing its parameters.

Dynamic Convolution adapts convolutional layers in neural networks based on input data characteristics.

Grouped Convolution is a technique that splits input channels into smaller groups for parallel processing in neural networks.

A pooling layer reduces the spatial dimensions of input data, retaining essential features while minimizing complexity.

Separable convolution is an efficient technique used in deep learning to reduce computation in convolutional neural networks.