Dataset distillation aims to synthesize a small number of images per class
(IPC) from a large dataset to approximate full dataset training with minimal
performance loss. While effective in very small IPC ranges, many distillation
methods become less effective, even underperforming random sample selection, as
IPC increases. Our examination of state-of-the-art trajectory-matching based
distillation methods across various IPC scales reveals that these methods
struggle to incorporate the complex, rare features of harder samples into the
synthetic dataset even with the increased IPC, resulting in a persistent
coverage gap between easy and hard test samples. Motivated by such
observations, we introduce SelMatch, a novel distillation method that
effectively scales with IPC. SelMatch uses selection-based initialization and
partial updates through trajectory matching to manage the synthetic dataset's
desired difficulty level tailored to IPC scales. When tested on CIFAR-10/100
and TinyImageNet, SelMatch consistently outperforms leading selection-only and
distillation-only methods across subset ratios from 5% to 30%.

SelMatch 是一种新颖的数据集精简方法，通过选择性初始化和轨迹匹配的部分更新，适应不同规模的 IPC，并在 CIFAR-10/100 和 TinyImageNet 数据集上从 5% 到 30% 的子集比率中始终胜过其他选择性筛选和仅精简方法。

SelMatch: 通过选择基础初始化和轨迹匹配的部分更新有效扩展数据集蒸馏

SelMatch: Effectively Scaling Up Dataset Distillation via  Selection-Based Initialization and Partial Updates by Trajectory Matching

Imitation learning holds tremendous promise in learning policies efficiently
for complex decision making problems. Current state-of-the-art algorithms often
use inverse reinforcement learning (IRL), where given a set of expert
demonstrations, an agent alternatively infers a reward function and the
associated optimal policy. However, such IRL approaches often require
substantial online interactions for complex control problems. In this work, we
present Regularized Optimal Transport (ROT), a new imitation learning algorithm
that builds on recent advances in optimal transport based trajectory-matching.
Our key technical insight is that adaptively combining trajectory-matching
rewards with behavior cloning can significantly accelerate imitation even with
only a few demonstrations. Our experiments on 20 visual control tasks across
the DeepMind Control Suite, the OpenAI Robotics Suite, and the Meta-World
Benchmark demonstrate an average of 7.8X faster imitation to reach 90% of
expert performance compared to prior state-of-the-art methods. On real-world
robotic manipulation, with just one demonstration and an hour of online
training, ROT achieves an average success rate of 90.1% across 14 tasks.

本文提出了一种基于正则化最优传输的模仿学习算法，结合了轨迹匹配奖励和行为克隆，大大提高了模仿学习的速度和效率，在 20 个视觉控制任务中的实验表明，比起之前的最先进方法，该算法可以平均快七倍，并且在真实世界的机器人操作中展现了高达 90% 的平均成功率。