Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 23 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2310.17347.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-22T06:31:00.163083+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-27T22:59:39.440344Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T13:29:52.042450Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation a2fb2050-0c24-4698-8b9d-d5aff5d9084e · inbound
MAGIC: Few-Shot Mask-Guided Anomaly Inpainting with Prompt Perturbation, Spatially Adaptive Guidance, and Context Awareness CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Reference 30
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 83bb0354-1f08-4760-8bc7-95fd71607ea8 · inbound
It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Reference 53
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 5c1755d7-874b-46c6-9eaf-1c78296cdd6d · inbound
A Universal Avoidance Method for Diverse Multi-branch Generation CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Reference 32
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 3b738794-db19-4fab-82cd-1ab47707215b · inbound
DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Reference 50
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation b04e1da0-d1a6-46a7-9b81-93e12c0db904 · inbound
STRIDE: Training-Free Diversity Guidance via PCA-Directed Feature Perturbation in Single-Step Diffusion Models CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Reference 32
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation cf56579f-1c86-45e9-8d14-d790867a57e5 · inbound
Breaking the Lock-in: Diversifying Text-to-Image Generation via Representation Modulation CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Reference 4
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 5523cba9-1a14-4bfb-b01b-4244b59e47f0 · inbound
Semantic Browsing: Controllable Diversity for Image Generation CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Reference 1
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 66f36abb-d7fd-49b6-85a2-02f533e55117 · inbound
Don't Settle at the Mode! Mitigating Diversity Collapse in Pretrained Flow Models via Feature Self-Guidance CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Reference 49
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.