Medical-imaging datasets are often smaller and more expensive to label than general image collections. Transfer learning helps by starting with a model that has already learned useful visual representations and adapting it to a new clinical task. Why pretrained features help Early neural-network layers detect patterns such as edges, shapes, and textures. Many of these…
Tag: Computer Vision
Why Residual Connections Make Deep Networks Easier to Train
Adding more layers should give a neural network more capacity, but very deep networks can become harder to optimize. Residual connections, popularized by ResNet, solve an important part of this problem and allow hundreds of layers to be trained effectively. The degradation problem A deeper model does not always achieve lower training error than a…
Understanding Convolutional Neural Networks for Image Recognition
Convolutional neural networks, usually called CNNs, are designed to learn visual patterns directly from images. They became a foundation of modern computer vision because they can recognize useful features without requiring a developer to describe every edge, texture, or shape manually. Images contain local patterns Nearby pixels are strongly related. A small group of pixels…