Fabrizio Musacchio<p>The <a href="https://sigmoid.social/tags/Wasserstein" class="mention hashtag" rel="tag">#<span>Wasserstein</span></a> distance (<a href="https://sigmoid.social/tags/EMD" class="mention hashtag" rel="tag">#<span>EMD</span></a>), sliced Wasserstein distance (<a href="https://sigmoid.social/tags/SWD" class="mention hashtag" rel="tag">#<span>SWD</span></a>), and the <a href="https://sigmoid.social/tags/L2norm" class="mention hashtag" rel="tag">#<span>L2norm</span></a> are common <a href="https://sigmoid.social/tags/metrics" class="mention hashtag" rel="tag">#<span>metrics</span></a> used to quantify the ‘distance’ between two distributions. This tutorial compares these three metrics and discusses their advantages and disadvantages.</p><p>🌎 <a href="https://www.fabriziomusacchio.com/blog/2023-07-26-wasserstein_vs_l2_norm/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">3-07-26-wasserstein_vs_l2_norm/</span></a></p><p><a href="https://sigmoid.social/tags/OptimalTransport" class="mention hashtag" rel="tag">#<span>OptimalTransport</span></a> <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="tag">#<span>MachineLearning</span></a></p>