2.4k citations · 2.5k across the 8 of their papers we have counts for
8 papers
Stable Distribution Alignment Using the Dual of the Adversarial Distance
Ben Usman, Kate Saenko, Brian Kulis
Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize w…
Learning a visuomotor controller for real world robotic grasping using simulated depth images
Ulrich Viereck, Andreas ten Pas, Kate Saenko +1
We want to build robots that are useful in unstructured real world applications, such as doing work in the household. Grasping in particular is an important skill in this domain, y…
Synthetic to Real Adaptation with Generative Correlation Alignment Networks
Xingchao Peng, Kate Saenko
Synthetic images rendered from 3D CAD models are useful for augmenting training data for object recognition algorithms. However, the generated images are non-photorealistic and do…
Correlation Alignment for Unsupervised Domain Adaptation
Baochen Sun, Jiashi Feng, Kate Saenko
In this chapter, we present CORrelation ALignment (CORAL), a simple yet effective method for unsupervised domain adaptation. CORAL minimizes domain shift by aligning the second-ord…
Modeling Relationships in Referential Expressions with Compositional Modular Networks
Ronghang Hu, Marcus Rohrbach, Jacob Andreas +2
People often refer to entities in an image in terms of their relationships with other entities. For example, "the black cat sitting under the table" refers to both a "black cat" en…
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision
Xingchao Peng, Kate Saenko
We present a novel approach to object classification and detection which requires minimal supervision and which combines visual texture cues and shape information learned from free…