  Dataset condensation aims to condense a large dataset with a lot of training
samples into a small set. Previous methods usually condense the dataset into
the pixels format. However, it suffers from slow optimization speed and large
number of parameters to be optimized. When increasing image resolutions and
classes, the number of learnable parameters grows accordingly, prohibiting
condensation methods from scaling up to large datasets with diverse classes.
Moreover, the relations among condensed samples have been neglected and hence
the feature distribution of condensed samples is often not diverse. To solve
these problems, we propose to condense the dataset into another format, a
generative model. Such a novel format allows for the condensation of large
datasets because the size of the generative model remains relatively stable as
the number of classes or image resolution increases. Furthermore, an
intra-class and an inter-class loss are proposed to model the relation of
condensed samples. Intra-class loss aims to create more diverse samples for
each class by pushing each sample away from the others of the same class.
Meanwhile, inter-class loss increases the discriminability of samples by
widening the gap between the centers of different classes. Extensive
comparisons with state-of-the-art methods and our ablation studies confirm the
effectiveness of our method and its individual component. To our best
knowledge, we are the first to successfully conduct condensation on
ImageNet-1k.
