What is DenseNet?

DenseNet (Densely Connected Convolutional Network) is a CNN architecture in which each layer receives the feature maps produced by every earlier layer within the same dense block. It was introduced to improve feature reuse and make deep networks easier to train.

How is DenseNet organized?

• Dense blocks: Each new layer concatenates all earlier feature maps with the features it creates. Later layers can reuse both low-level and high-level information.
• Transition layers: Layers between dense blocks typically use a 1×1 convolution and pooling to reduce feature-map size and manage computation.
• Classifier: The final features are used for a prediction, such as an image class.

A simplified flow is:

Input → Dense Block → Transition → Dense Block → Classifier

Practical example

Suppose a dense block starts with 16 channels, and each layer produces 8 new channels. The available channels grow as:

16 → 24 → 32 → 40

The number of new channels contributed by each layer (here, 8) is called the growth rate.

Why use DenseNet?

• Feature reuse: Layers can access earlier features directly instead of relearning them.
• Improved gradient flow: Direct connections create short paths back to early layers.
• Parameter efficiency: Reusing features can achieve strong performance with relatively few learned parameters.

One trade-off is memory usage: concatenating and retaining many feature maps can require substantial memory.

DenseNet versus ResNet: DenseNet concatenates features, while a typical ResNet block adds its input to a learned transformation. The next article, “How does DenseNet differ from ResNet?”, explores this distinction.

For the underlying connection pattern, see “What is a dense connection in a neural network?”.

Original paper: Densely Connected Convolutional Networks.