mashijie1028/TrustDD
(Pattern Recognition 2025) Towards Trustworthy Dataset Distillation
This helps deep learning practitioners create smaller, synthetic datasets that efficiently train models for image classification and detect unusual, 'out-of-distribution' images. You input a large, real image dataset, and it outputs a tiny, distilled dataset. Data scientists or machine learning engineers in charge of deploying reliable deep learning models would use this.
No commits in the last 6 months.
Use this if you need to train deep learning models efficiently on smaller datasets while ensuring they can reliably identify data that doesn't fit their training distribution, making them safer for real-world use.
Not ideal if your primary concern is solely in-distribution classification performance without any need for identifying out-of-distribution data or if you prefer to use the full, original dataset for training.
Stars
14
Forks
—
Language
Python
License
MIT
Category
Last pushed
Dec 08, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mashijie1028/TrustDD"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Guang000/Awesome-Dataset-Distillation
A curated list of awesome papers on dataset distillation and related applications.
dkozlov/awesome-knowledge-distillation
Awesome Knowledge Distillation
SforAiDl/KD_Lib
A Pytorch Knowledge Distillation library for benchmarking and extending works in the domains of...
SakurajimaMaiii/ProtoKD
[ICASSP 2023] Prototype Knowledge Distillation for Medical Segmentation with Missing Modality
HikariTJU/LD
Localization Distillation for Object Detection (CVPR 2022, TPAMI 2023)