AIdventure.

The Receptive Field in Convolutional Neural Networks

Why do architectures use 3x3 filters? It is because of something called Receptive Fields.

SENet - Squeeze and Excitation Networks

Introducing channel attention to improve the performance of image classification tasks

MobileNet v1 - Efficient Convolutional Neural Networks

Efficient convolutional neural networks for mobile vision applications

DenseNet - Densely Connected Convolutional Networks

Connecting each layer to every other layer to maximize information flow and efficiency

Depthwise Separable Convolutions

Same results as standard convolutions with only a fraction of the computational cost. Explore the tricks behind MobileNet and efficient CNNs

ResNet - Deep Residual Learning for Image Recognition

Learn how residual blocks help solve the vanishing gradient problem to enable training of extremely deep neural networks

VGG - Very Deep Convolutional Networks

Explore how VGG revolutionized computer vision by using small 3x3 filters to build deeper networks