28 lines
1.8 KiB
Markdown
28 lines
1.8 KiB
Markdown
# Method Examples
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Methods are the mathematical techniques used to ask "what pixels mattered to this model's decision?" Each produces a fundamentally different visual character.
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> model: vgg16 · composition: pure_white · colormap: magma · intensity: 0.75 · contrast: 1.5 · threshold: 0.25 · class_index: -1 · input_size: 224 · use_smoothgrad: true · smoothgrad_samples: 25 · **method: varies**
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| Method | Approach | Character |
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|--------|----------|-----------|
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| **Vanilla Gradients** | Derivative of output w.r.t. each pixel | Raw, noisy, chaotic — every pixel with any influence at all, including spurious ones |
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| **SmoothGrad** | Vanilla Gradients averaged over many noise-perturbed runs | Smoother and more stable than vanilla, but still texture-rich |
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| **Integrated Gradients** | Accumulates gradients along a path from black image → input | Clean, theoretically grounded, highlights semantically meaningful regions. Usually the best starting method. |
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| **Blur IG** | Like IG, but interpolates from a blurred image → sharp input | Focused on edges and structural transitions rather than color/texture. More architectural. |
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| **Guided IG** | IG with an adaptive path that concentrates steps where gradients are largest | Sharpest, highest-contrast results. Most visually striking for portraits. |
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| **XRAI** | Segments image into regions, attributes importance per region rather than per pixel | Patchwork/stained glass aesthetic — very different from gradient methods. Slower. |
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**Vanilla Gradients** 
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**SmoothGrad** 
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**Integrated Gradients** 
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**Blur IG** 
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**Guided IG** 
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**XRAI**  |