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Found 4 papers, 2 papers with code
AutoNovel: Automatically Discovering and Learning Novel Visual Categories
We present a new approach called AutoNovel to address this problem by combining three ideas: (1) we suggest that the common approach of bootstrapping an image representation using the labelled data only introduces an unwanted bias, and that this can be avoided by using self-supervised learning to train the representation from scratch on the union of labelled and unlabelled data; (2) we use ranking statistics to transfer the model's knowledge of the labelled classes to the problem of clustering the unlabelled images; and, (3) we train the data representation by optimizing a joint objective function on the labelled and unlabelled subsets of the data, improving both the supervised classification of the labelled data, and the clustering of the unlabelled data.
Ranked #1 on
Novel Class Discovery
on SVHN
Unsupervised Intuitive Physics from Past Experiences
We are interested in learning models of intuitive physics similar to the ones that animals use for navigation, manipulation and planning.
Learning to Represent Mechanics via Long-term Extrapolation and Interpolation
While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and associated parameters.
Stopping GAN Violence: Generative Unadversarial Networks
While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed.
