Dilated Conv & Receptive Field Calculator
Effective kernel size and stacked receptive field of dilated (atrous) convolutions — WaveNet/DeepLab style.
Defaults are one WaveNet block: ten 3-tap layers with dilations 1…512 see 2,047 samples — an undilated stack would need 1,023 layers for the same span.
Formula
About Dilated Conv & Receptive Field Calculator
Dilated convolutions are the trick that lets a handful of small kernels see thousands of samples: spread the taps apart by dilation d and the kernel spans d·(k−1)+1 positions at unchanged cost. Stack layers with doubling dilations and the receptive field grows exponentially while parameters grow linearly — WaveNet's founding insight, later recycled by DeepLab for semantic segmentation and by TCNs for forecasting. This calculator computes effective kernel spans and total stacked receptive fields for the three standard dilation schemes, with the comparison against an undilated stack made explicit.
How to use Dilated Conv & Receptive Field Calculator
- 1Enter your values into Dilated Conv & Receptive Field Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Dilated Conv & Receptive Field Calculator?
- ✓Computes Dilated Conv & Receptive Field instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: effective k = d.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why do dilated stacks double the dilation each layer?+
Doubling tiles the input without gaps or overlap: layer i covers stride-2^i patterns, and together layers 1..n see every position in a 2^n window exactly once per path. Constant dilation >1 instead leaves periodic holes — the 'gridding' artifact DeepLab papers warn about.
Is receptive field the same as 'what the model actually uses'?+
No — the theoretical RF computed here is an upper bound. Effective receptive fields measured empirically (Luo et al. 2016) are Gaussian-ish and much smaller; central inputs dominate. Still, theoretical RF must at least cover your signal's correlation length.
Dilated conv vs attention for long sequences?+
Dilated stacks cost O(n) per layer and excel at fixed local-to-global hierarchies (audio, time series); attention costs O(n²) but learns arbitrary dependencies. TCN-vs-transformer benchmarks split by task; for raw audio at 16 kHz+, dilation remains the pragmatic choice.
How does this relate to DeepLab's ASPP?+
Atrous Spatial Pyramid Pooling runs parallel branches at dilations like 6/12/18 on a 3×3 kernel — effective kernels of 13/25/37 px — capturing multi-scale context in one layer. Enter each dilation here (constant scheme, 1 layer) to size the branches.
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