When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix
To overcome data scarcity and privacy constraints in data collection, it has become standard practice across academia and industry to augment real training data with text-to-image (T2I)-generated synthetic data, a paradigm we term Real-Synt
https://arxiv.org/abs/2607.13541v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
Light
How close the company’s idea is to your thesis statement
Sector
ai
Overlap with sectors you care about
Geography
Unknown
Location unknown — scores 0
Your thesis: “We back exceptional technical founders building AI-first products and infrastructure, deploying $100K checks within 24 hours.”
Founder → stable
Traction → stable
Idea vs market ↓ declining
▸ Add / edit details
Correct facts used on the next screening or memo.
People
N
Na Li
1.0
low confidence
B
Boyu Kuang
1.0
low confidence
H
Hongsheng Hu
1.0
low confidence
L
Liquan Chen
1.0
low confidence
H
Hyoungshick Kim
1.0
low confidence
Y
Yansong Gao
1.0
low confidence
A
Anmin Fu
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
Zumo Labs
· Inactive
We generate synthetic data for computer vision models.
founders not scraped yet