How does DenseNet differ from ResNet?

DenseNet and ResNet both use shortcut connections to train deep convolutional networks, but they combine earlier features differently: DenseNet concatenates them, while ResNet adds them.

Key differences

• How layers connect: In ResNet, a residual block adds the input to a learned transformation: y = x + F(x). In DenseNet, each layer receives a concatenation of all earlier feature maps in the same dense block.
• What happens to channels: Residual addition normally preserves the output channel count. DenseNet concatenation increases the available channel count as each layer contributes new feature maps.
• What the network learns: ResNet learns corrections to existing representations. DenseNet encourages explicit reuse of previously computed features.
• Efficiency trade-offs: DenseNet can achieve strong parameter efficiency through feature reuse, but retaining and concatenating many feature maps can increase activation-memory demands. Actual speed and memory depend on the implementation and model configuration.

Simple example

Suppose the earlier features are x and a new layer produces F(x):

• ResNet: x + F(x) — combines corresponding values through addition.
• DenseNet: [x, F(x)] — keeps both sets of features as separate channels.

That difference determines how information is reused by subsequent layers.

Which should you choose?

• ResNet is a strong general-purpose CNN baseline with widely used residual blocks.
• DenseNet is worth considering when feature reuse and parameter efficiency are priorities, provided its feature-map memory costs fit the deployment and training setup.

Neither architecture is universally better; compare validation accuracy, latency, and memory for the actual task.

For background, see “What is DenseNet?”, “What is a ResNet?”, and “What is a dense connection in a neural network?”.

Original papers: Deep Residual Learning for Image Recognition and Densely Connected Convolutional Networks.