ServiceNow Research

Unsupervised Learning

Group Robust Classification Without Any Group Information
Empirical risk minimization (ERM) is sensitive to spurious correlations present in training data, which poses a significant risk when …
Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with …
Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with …
Overcoming challenges in leveraging GANs for few-shot data augmentation
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. …
Unsupervised Model-based Pre-training for Data-efficient Reinforcement Learning from Pixels
Reinforcement learning (RL) aims at autonomously performing complex tasks. To this end, a reward signal is used to steer the learning …
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use …
Unsupervised Learning of Dense Visual Representations
Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning. In general, …
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
Generative adversarial networks have been very successful in generative modeling, however they remain relatively challenging to train …
Neocortical plasticity: an unsupervised cake but no free lunch
The fields of artificial intelligence and neuroscience have a long history of fertile bi-directional interactions. On the one hand, …
On Adversarial Mixup Resynthesis
In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We …