<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/">
  <channel>
    <title>Training-Data on The Kiseki Log</title>
    <link>https://ki-seki.github.io/tags/training-data/</link>
    <description>Recent content in Training-Data on The Kiseki Log</description>
    <generator>Hugo -- 0.146.0</generator>
    <language>en</language>
    <copyright>2023-2026 Elliot Shichao Song CC BY-SA 4.0</copyright>
    <lastBuildDate>Sun, 25 Oct 2026 22:30:44 +0800</lastBuildDate>
    <atom:link href="https://ki-seki.github.io/tags/training-data/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>A Billion-Scale Retrieval System for Text-to-Image Training Data</title>
      <link>https://ki-seki.github.io/posts/261025-training-data-retrieval/</link>
      <pubDate>Sun, 25 Oct 2026 20:12:06 +0800</pubDate>
      <guid>https://ki-seki.github.io/posts/261025-training-data-retrieval/</guid>
      <description>Training a text-to-image model from scratch means finding, inspecting and filtering samples across billions of image–text pairs. Using a retrieval system over 1.4B samples as an example, this post covers data storage and management, distributed embedding extraction, building and querying an IVF-PQ index, and online retrieval and cluster browsing.</description>
    </item>
  </channel>
</rss>
