Buckets:
| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8" /> | |
| <title>Reproduction poster — DFMPQ</title> | |
| <style> | |
| html, body { margin: 0; padding: 0; background: #f7f7f6; | |
| font-family: "Helvetica Neue", "Arial", sans-serif; color: #1f2937; } | |
| .poster { width: 100%; max-width: 1300px; margin: 0 auto; background: #fff; | |
| border: 1px solid #e5e7eb; border-radius: 14px; overflow: hidden; } | |
| .masthead { background: linear-gradient(120deg, #0F6070 0%, #022733 100%); | |
| color: #fff; padding: 24px 32px; } | |
| .masthead .eyebrow { font-size: 12px; letter-spacing: .18em; | |
| text-transform: uppercase; color: #cffafe; | |
| font-weight: 700; } | |
| .masthead h1 { font-size: 26px; line-height: 1.15; margin: 8px 0 4px; | |
| font-weight: 700; } | |
| .masthead .venue { font-size: 13px; opacity: .9; } | |
| .grid { display: grid; grid-template-columns: 1fr 1fr 1fr 1fr; | |
| gap: 16px; padding: 20px 24px; } | |
| .card { background: #f8fafc; border: 1px solid #e5e7eb; border-radius: 10px; | |
| padding: 14px 16px; min-height: 140px; } | |
| .card.full { grid-column: 1 / span 4; } | |
| .card .tag { font-size: 10px; letter-spacing: .15em; | |
| text-transform: uppercase; color: #64748b; | |
| font-weight: 700; } | |
| .card h3 { margin: 6px 0 8px; font-size: 16px; color: #0F6070; } | |
| .card p, .card li { font-size: 12.5px; line-height: 1.5; margin: 0 0 6px; | |
| color: #1f2937; } | |
| .card ul { padding-left: 16px; margin: 0 0 6px; } | |
| .card .big { font-size: 22px; font-weight: 800; color: #0F6070; | |
| line-height: 1; } | |
| .card table { font-size: 11.5px; width: 100%; border-collapse: collapse; } | |
| .card th, .card td { padding: 4px 6px; text-align: right; | |
| border-bottom: 1px dotted #e2e8f0; } | |
| .card th { text-align: left; color: #475569; font-weight: 600; } | |
| .strip { background: #0F6070; color: #fff; padding: 16px 24px; | |
| font-size: 13px; line-height: 1.5; } | |
| .strip b { color: #cffafe; } | |
| .target { cursor: pointer; outline: none; border: 0; background: transparent; | |
| color: inherit; font: inherit; padding: 0; margin: 0; | |
| text-decoration: none; } | |
| .target:hover { box-shadow: inset 0 -2px 0 0 #cffafe; } | |
| .footnote { font-size: 10.5px; color: #64748b; padding: 0 24px 18px; } | |
| </style> | |
| </head> | |
| <body> | |
| <section class="poster" data-measure-role="poster"> | |
| <div class="masthead"> | |
| <div class="eyebrow">ICML 2026 reproduction · OpenReview nHbvQF35ch</div> | |
| <h1>No Retraining at Edge: Efficient Resource-Aware Mixed-Precision Quantization | |
| via Federated Supernet Learning</h1> | |
| <div class="venue">Independent toy-scale reproduction · paper authors: | |
| Lianbo Ma, Yonghui Su, Nan Li, Xingwei Wang</div> | |
| </div> | |
| <section class="grid"> | |
| <div class="card" data-logbook-target="executive-summary"> | |
| <div class="tag">Executive summary ↗</div> | |
| <h3>Headline outcome</h3> | |
| <p><span class="big">Partial</span> reproduction at toy scale.</p> | |
| <p>Claim 1 reproduces via <b>SAGS</b> (paper §4.3): fed-Avg-trained | |
| supernet serves <b>20 deployment budgets</b> derivation-only | |
| (zero gradient steps). Claim 2 partially reproduces: | |
| DFMPQ mixed-bit beats same-size uniform subnet in several budgets.</p> | |
| </div> | |
| <div class="card" data-logbook-target="claim-1-dfmpq-enables-retraining-free-deployment-for-dynamic-edge-computing-by-deriving-resource-aware-quantized-subnets-on-demand"> | |
| <div class="tag">Claim 1 ↗</div> | |
| <h3>Retraining-free at the edge</h3> | |
| <p>4 supernets (ResNet-8 + tiny MobileNetV2 × CIFAR-10/100). Load | |
| checkpoints, derive subnets for 5 memory budgets per supernet via | |
| <b>SAGS</b> (paper §4.3 — sensitivity-ranked greedy downgrade, | |
| top-K=3, no gradient steps) — <b>zero training-time gradient | |
| steps</b>, ~30 s wall-clock total.</p> | |
| <p>Implements <b>CCSA</b> (§4.1 — linear-MMD class-conditional | |
| prototype alignment, λ=0.1) and <b>SAHA</b> (§4.2 — | |
| semantic-confidence aggregation on crucial stage-output layers, | |
| τ=1.0) for supernet training, matching the paper's algorithm.</p> | |
| </div> | |
| <div class="card" data-logbook-target="claim-2-achieves-competitive-accuracy-with-significantly-reduced-computational-cost-across-multiple-datasets-and-network-architectures"> | |
| <div class="tag">Claim 2 ↗</div> | |
| <h3>Competitive vs reduced compute</h3> | |
| <p>Per (dataset × arch), mean Δ(DFMPQ − best uniform) — toy scale:</p> | |
| <table> | |
| <tr><th>pair</th><th>Δ (pp)</th></tr> | |
| <tr><td>c10/ResNet-8</td><td>+6.06</td></tr> | |
| <tr><td>c10/MobileV2</td><td>+2.38</td></tr> | |
| <tr><td>c100/ResNet-8</td><td>+0.61</td></tr> | |
| <tr><td>c100/MobileV2</td><td>+1.23</td></tr> | |
| </table> | |
| <p>Absolute acc far below paper — paper-scale (10×50×5) expected to close gap.</p> | |
| </div> | |
| <div class="card"> | |
| <div class="tag">Scope & cost</div> | |
| <h3>What we spent</h3> | |
| <p><b>HW</b>: 1× RTX 3050 laptop GPU (4 GB) + 16 CPU cores.</p> | |
| <p><b>Wall</b>: ≈ 22 min (~20 min training 4 supernets with CCSA+SAHA + | |
| ~1 min SAGS derivation).</p> | |
| <p><b>Cost</b>: ≈ $0 (local). HF Jobs returned HTTP 402 | |
| (account out of credits).</p> | |
| <p><b>Scope gap</b>: paper ResNet-18/MNetV2/EffNet-Lite0, | |
| 10 clients × 50 rounds × 5 epochs, batch 256; we ResNet-8/tiny-MNetV2, | |
| 5 × 15 × 2, batch 128, 2 datasets.</p> | |
| </div> | |
| <div class="card full" data-logbook-target="conclusion"> | |
| <div class="tag">Reproduction bundle ↗</div> | |
| <h3>What we publish</h3> | |
| <p>Self-contained bundle (3.6 MB) containing | |
| <code>repro_dfmpq/dfmpq.py</code> (STE quantizer + QResNet8 / QMobileTiny | |
| supernets + Dirichlet non-IID partition + <b>CCSA</b> regularizer + | |
| <b>SAHA</b> semantic aggregation + <b>SAGS</b> search + FedAvg), | |
| <code>repro_dfmpq/run.py</code> (with flags --no-ccsa, --no-saha, | |
| --lambda-ccsa, --saha-tau), <code>repro_dfmpq/derive_only.py</code> | |
| (Claim 1 demo, SAGS derivation only), <code>repro_dfmpq/make_plots.py</code>, | |
| <b>4 trained supernet checkpoints</b> (one per dataset × arch), | |
| <b>results.csv</b> (every (dataset × arch × budget × method) row), | |
| <b>Plotly Pareto-frontier HTML figures</b>, and a verbatim training | |
| log. Bundled as a Trackio <b>dataset artifact</b> on the Conclusion | |
| page; rerunnable in ≈ 25 min on any single GPU.</p> | |
| </div> | |
| </section> | |
| <div class="strip"> | |
| Bottom line: <b>Claim 1 mechanism reproduces</b> — one federated supernet | |
| (CCSA + SAHA during training) serves many edge budgets by <b>SAGS</b> | |
| derivation only, no retraining. <b>Claim 2 partially reproduces</b> at | |
| toy scale (DFMPQ > same-size uniform baseline on all 4 (dataset × arch) | |
| pairs); absolute accuracy far below paper because paper is 10 clients × | |
| 50 rounds × 5 epochs on A100 with ResNet-18; we are 5 × 15 × 2 on a 4 GB | |
| laptop GPU with ResNet-8. Gap to per-budget-retrain reference (~10–18 pp) | |
| is the expected toy-scale signature. | |
| </div> | |
| <div class="footnote"> | |
| Built with the OpenResearch reproduction agent using the | |
| <a href="https://github.com/Chenruishuo/posterely">Chenruishuo/posterly</a> | |
| HTML poster skill. Hover over an underlined <code>↗</code> tag and click to | |
| jump to the matching logbook page; this poster embeds no external assets | |
| — it is fully self-contained. | |
| </div> | |
| </section> | |
| </body> | |
| </html> |
Xet Storage Details
- Size:
- 7.71 kB
- Xet hash:
- dee3546e4cf6081a0f57295d91a13da15ba5c9e5fff36c5c96dfddca2579493a
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.