Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress 60, 000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to original performance with only a few gradient descent steps, given a fixed network initialization. We evaluate our method in various initialization settings and with different learning objectives. Experiments on multiple datasets show the advantage of our approach compared to alternative methods.

We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training, we introduce the largest public light field dataset, consisting of over 3300 plenoptic camera light fields of scenes containing flowers and plants. Our synthesis pipeline consists of a convolutional neural network (CNN) that estimates scene geometry, a stage that renders a Lambertian light field using that geometry, and a second CNN that predicts occluded rays and non-Lambertian effects. Our algorithm builds on recent view synthesis methods, but is unique in predicting RGBD for each light field ray and improving unsupervised single image depth estimation by enforcing consistency of ray depths that should intersect the same scene point.

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The electrochemical oxidation of small molecules to generate value‐added products has gained enormous interest in recent years because of the advantages of benign operation conditions, high conversion efficiency and selectivity, the absence of external oxidizing agents, and eco‐friendliness. Coupling the electrochemical oxidation of small molecules to replace oxygen evolution reaction (OER) at the anode and the hydrogen evolution reaction (HER) at the cathode in an electrolyzer would simultaneously realize the generation of high‐value chemicals or pollutant degradation and the highly efficient production of hydrogen. This Minireview presents an introduction on small‐molecule choice and design strategies of electrocatalysts as well as recent breakthroughs achieved in the highly efficient production of hydrogen. Finally, challenges and future orientations are highlighted.