AIdventure.

DINO - Emerging Properties in Self-Supervised Vision Transformers

Unsupervised visual feature learning using knowledge distillation and transformers

CLIP - Contrastive Language-Image Pre-training

Contrastive learning for unified vision-language representations in a shared embedding space

Vision Transformer - An Image is Worth 16x16 Words

Google shows how treating image patches as tokens can revolutionize computer vision

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