Judy Hoffman

TL;DR: Adversarial Discriminative Domain Adaptation (ADDA) as mentioned in this paper combines discriminative modeling, untied weight sharing, and a generative adversarial network (GAN) loss.

TL;DR: DeCAF as discussed by the authors is an open-source implementation of these deep convolutional activation features, along with all associated network parameters, to enable vision researchers to conduct experimentation with deep representations across a range of visual concept learning paradigms.

TL;DR: DeCAF, an open-source implementation of deep convolutional activation features, along with all associated network parameters, are released to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.

TL;DR: A novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model that adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs is proposed.

TL;DR: This work proposes a new CNN architecture which introduces an adaptation layer and an additional domain confusion loss, to learn a representation that is both semantically meaningful and domain invariant and shows that a domain confusion metric can be used for model selection to determine the dimension of an adaptationlayer and the best position for the layer in the CNN architecture.