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DmitryRyumin 
posted an update Oct 27, 2025
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1952
🚀🏷️🌟 New Research Alert - ICCV 2025 (Oral)! 🌟🧩🚀
📄 Title: Heavy Labels Out! Dataset Distillation with Label Space Lightening 🔝

📝 Description: The HeLlO framework is a new corpus distillation method that removes the need for large soft labels. It uses a lightweight, online image-to-label projector based on CLIP. This projector has been adapted using LoRA-style, parameter-efficient tuning. It has also been initialized with text embeddings.

👥 Authors: @roseannelexie , @Huage001 , Zigeng Chen, Jingwen Ye, and Xinchao Wang

📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸

📄 Paper: Heavy Labels Out! Dataset Distillation with Label Space Lightening (2408.08201)

📺 Video: https://www.youtube.com/watch?v=kAyK_3wskgA

🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers

🚀 Added to the Efficient Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/efficient-learning.md

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #DatasetDistillation #LabelCompression #CLIP #LoRA #EfficientAI #FoundationModels #AI #ICCV2025 #ResearchHighlight

Massive Alienating Mandate Binding Appellation, Out!

Cool! How ironic, while industrial teams are struggling daily to build large enough datasets to build robust AI applications, others are struggling with their massive dataset 😅