Vision · 2015 · residual learning
ResNet
Residual networks make depth trainable by letting layers learn corrections to the identity: each block computes F(x) + x instead of an unreferenced transform.
ModLensVision
Interactive Diagram
Focus the lens
Click any component to read what it does. Signal direction follows the edges.
inputInput. Feature map entering the block.
Core idea
If a deeper network should at least match a shallower one, parameterize the difference. Skip connections give gradients a highway and depth stops being the enemy.
Why it exists
Plain deep networks degrade during training long before overfitting; residuals reframe depth as a series of learnable refinements.
Mathematics
y = \mathcal{F}(x, \{W_i\}) + x
Data Flow
What moves through the system
- 01Input passes through a stem convolution.
- 02Residual blocks add learned corrections to their own input.
- 03Spatial downsampling stages widen the representation.
- 04Global pooling and a head produce the output.
Strengths
- + Trains reliably at great depth
- + Skip connections stabilize optimization
- + The default backbone for a decade of vision systems
Limitations
- − Still local-first: long-range context needs depth
- − Uniform compute across the image
Applications
- · Backbones for detection/segmentation
- · Medical imaging
- · Any task needing a proven visual encoder