pith. sign in
Pith Number

pith:I4KKES37

pith:2026:I4KKES37KHTVIWXYFA2AAVOJQD
not attested not anchored not stored refs pending

SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation

Ce Wang, Wanjie Sun, Zhenyu Hu

SlimDiffSR distills a diffusion model into a single-step student network that runs 200 times faster and uses 20 times fewer parameters for remote sensing super-resolution.

arxiv:2605.02198 v2 · 2026-05-04 · cs.CV

Add to your LaTeX paper
\usepackage{pith}
\pithnumber{I4KKES37KHTVIWXYFA2AAVOJQD}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp
2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
Portable graph bundle live · download bundle · merged state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

SlimDiffSR attains up to 200× inference acceleration and a 20× reduction in model parameters compared with multi-step diffusion models, while achieving competitive perceptual quality and clearly outperforming existing lightweight diffusion baselines in efficiency.

C2weakest assumption

That the uncertainty-guided timestep assignment and structured pruning with frequency- and direction-separable convolutions plus MMD distillation will preserve generative quality on remote sensing data without significant degradation or domain-specific failure modes.

C3one line summary

SlimDiffSR uses uncertainty-guided timestep assignment and remote-sensing-tailored pruning plus MMD distillation to create a diffusion SR model with 200x faster inference and 20x fewer parameters while keeping competitive quality.

Receipt and verification
First computed 2026-05-20T00:04:33.585516Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

4714a24b7f51e7545af828340055c980e5c0d89c1353898192a3e25ea087fc4c

Aliases

arxiv: 2605.02198 · arxiv_version: 2605.02198v2 · doi: 10.48550/arxiv.2605.02198 · pith_short_12: I4KKES37KHTV · pith_short_16: I4KKES37KHTVIWXY · pith_short_8: I4KKES37
Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/I4KKES37KHTVIWXYFA2AAVOJQD \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 4714a24b7f51e7545af828340055c980e5c0d89c1353898192a3e25ea087fc4c
Canonical record JSON
{
  "metadata": {
    "abstract_canon_sha256": "294f8f4e658abc77b6346d72a10c771f0a50a5a800a04a7c114784dac7848a0c",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-04T03:54:54Z",
    "title_canon_sha256": "afdafc3d8262a5924e37a6b40c2de3c2a8567232cec41a391b91b8d77bf8f8e6"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.02198",
    "kind": "arxiv",
    "version": 2
  }
}