arXiv (All)
open access
A Causal Bootstrap for Diverse Image Generation under Unobserved Confounding
Deep generative models reproduce the observational distribution underlying the training data, which, in turn, inherit any spurious associations it contains. A common source is an unobserved confounder that simultaneously affects both a treatment attribute the user wants to control/edit (e.g., age, s
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
- Structure-agnostic Causal Representation Learning#1155903
- Exploring Sparse Autoencoders in Text-Based Causal Confounding Adjustment#627703
- Tracing Generated Samples to Training-Data Clusters in Flow-Matching Models#924658
- Tracing Generated Samples to Training-Data Clusters in Flow-Matching Models#920424
- HiRAE: Hierarchical Representation Autoencoding with Residual Budgets#1142084
Record · ID 1455435
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