451 citations · 820 across the 3 of their papers we have counts for
4 papers
Adversarial Discriminative Domain Adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko +1
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They also can improve recognition d…
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Baochen Sun, Kate Saenko
Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input dist…
Fine-to-coarse Knowledge Transfer For Low-Res Image Classification
Xingchao Peng, Judy Hoffman, Stella X. Yu +1
We address the difficult problem of distinguishing fine-grained object categories in low resolution images. Wepropose a simple an effective deep learning approach that transfers fi…
What Do Deep CNNs Learn About Objects?
Xingchao Peng, Baochen Sun, Karim Ali +1
Deep convolutional neural networks learn extremely powerful image representations, yet most of that power is hidden in the millions of deep-layer parameters. What exactly do these…