Kate Saenko

Return of Frustratingly Easy Domain Adaptation

Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical sc...

Moment Matching for Multi-Source Domain Adaptation

Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain. This neglects the more practical scenario where training data are ...

Adapting Visual Category Models to New Domains

From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains

The most successful 2D object detection methods require a large number of images annotated with object bounding boxes to be collected for training. We present an alternative app...

Deep Domain Confusion: Maximizing for Domain Invariance

Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning ...

ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes

Less than 35% of recyclable waste is being actually recycled in the US [2], which leads to increased soil and sea pollution and is one of the major concerns of environmental res...

Long-term Recurrent Convolutional Networks for Visual Recognition and Description

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent are effective for tasks invo...