arXiv (All)
open access
D3S2: Diffusion-Guided Dataset Distillation for Semantic Segmentation
Dataset distillation (DD) aims to compress large-scale datasets into compact synthetic sets while preserving training efficacy. However, existing studies mainly focus on image classification, leaving dense prediction tasks such as semantic segmentation largely underexplored. In this work, we identif
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
- VCAR: Training-Free 3DGS Segmentation via View Completeness and Axis-Aware Boundary Refinement#818910
- VCAR: Training-Free 3DGS Segmentation via View Completeness and Axis-Aware Boundary Refinement#808918
- Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training#820192
- Slot-RAE: Streamlining Object-Centric Learning via Direct Representation Auto-Encoders#1129330
- RESAMPL-UDA: Leveraging foundation models for unsupervised domain adaptation in biomedical images#922978
Record · ID 1217473
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