Most planning formalisms allow instances with shortest plans of exponential length. While such instances are problematic, they are usually unavoidable and can occur in practice. There are several known cases of restricted planning problems where plans can be exponential but always have a compact (ie.
There are multiple and even interacting dimensions along which shape representation schemes may be compared and contrasted. In this paper, we focus on the following ques- tion. Are the building blocks in a compositional model lo- calized in space (e.g. as in part based representations) or are they holistic simplifications (e.g. as in spectral representa- tions)? Existing shape representation schemes prefer one or the other. We propose a new shape representation paradigm that encompasses both choices.
Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as compelling results in unconditional image generation.
We would like to learn a representation of the data that reflects the semantics behind a specific grouping of the data, where within a group the samples share a common factor of variation. For example, consider a set of face images grouped by identity. We wish to anchor the semantics of the grouping into a disentangled representation that we can exploit. However, existing deep probabilistic models often assume that the samples are independent and identically distributed, thereby disregard the grouping information. We present the Multi-Level Variational Autoencoder (ML-VAE), a new deep probabilistic model for learning a disentangled representation of grouped data.