The Noise-Transmission Burden of Diffusion Network: Why Image Prediction Can Outperform Noise Prediction
Overview & Synopsis
Clean-image prediction ($x$-prediction) and noise prediction yield equivalent population-optimal denoisers, yet can perform very differently in finite diffusion networks. For noisy input $Y_t$ with noise standard deviation $σ_t$, a noise predictor $f_{\varepsilon,t}$ induces the clean-image estimate $\widehat X_1=Y_t-σ_t f_{\varepsilon,t}(Y_t)$. This reveals a noise-transmission burden: the predictor must preserve information needed to cancel the input corruption. Under Gaussian corruption of $d$-dimensional data, a linear input bottleneck of rank $r
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Document Information
| Document Title | The Noise-Transmission Burden of Diffusion Network: Why Image Prediction Can Outperform Noise Prediction |
|---|---|
| Author / Organization | Gyeonghun Kang, Pengxi Liu, Bingshuo Qian, Xiang Cheng |
| Subject & Discipline | stat.ML |
| Categories | Computer Science & Mathematics |
| Publication Year | 2026 |
| License / Rights | arXiv Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
arXiv
(arxiv.org)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-5975 |
| Original Source Link | Visit Source Page on arxiv.org » |