arXiv (OAI Expanded)
open access
APEX: Assumption-free Projection-based Embedding eXamination Metric for Generative Distribution Evaluation
As generative models achieve unprecedented visual quality, the gold standard for image evaluation remains traditional feature-distribution metrics. However, these metrics are provably hindered by the closed-vocabulary bottleneck of outdated features and the assumptive bias of rigid parametric formul
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels#130752
- UEmbed: Unified Sparse and Dense Multimodal Embeddings#424000
- TRIG: Toward Measuring Texture Realism in Image Generation#1326414
- One Geometry, Different Outcomes: Readout-Dependent Effects of the Modality Gap in Vision-Language Models#1130943
- From Glance to Scrutiny: Progressive Distortion Reasoning for Fine-Grained Image Quality Assessment#985680
Record · ID 1428949
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive