Dataset Condensation with Distribution Matching

Computational cost of training state-of-the-art deep models in many learning problems is rapidly increasing due to more sophisticated models and larger datasets. A recent promising direction for reducing training cost is dataset condensation that aims to replace the original large training set with a significantly smaller learned synthetic set while preserving the original information. While training deep models on the small set of condensed images can be extremely fast, their synthesis remains computationally expensive due to the complex bi-level optimization and secondorder derivative computation. In this work, we propose a simple yet effective method that synthesizes condensed images by matching feature…

• CS
  China Scholarship Council
• EA
  Engineering and Physical Sciences Research Council

  Award: EP/T028572/1