One-Shot Adaptation of Supervised Deep Convolutional Models
Authors: Trevor Darrell, Eric Tzeng, Yangqing Jia, Judy Hoffman, Kate Saenko, Jeff Donahue
ICLR 2014

Abstract: Dataset bias remains a significant barrier towards solving real world computer vision tasks. Though deep convolutional networks have proven to be a competitive approach for image classification, a question remains: have these models have solved the dataset bias problem? In general, training or fine-tuning a state-of-the-art deep model on a new domain requires a significant amount of data, which for many applications is simply not available. Transfer of models directly to new domains without adaptation has historically led to poor recognition performance. In this paper, we pose the following question: is a single image dataset, much larger than previously explored for adaptation, comprehensive enough to learn general deep models that may be effectively applied to new image domains? In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be? We show that a generic supervised deep CNN model trained on a large dataset reduces, but does not remove, dataset bias. Furthermore, we propose several methods for adaptation with deep models that are able to operate with little (one example per category) or no labeled domain specific data. Our experiments show that adaptation of deep models on benchmark visual domain adaptation datasets can provide a significant performance boost.


Peer review:
[Review]:
Thank you to all the reviewers for your comments and suggestions. We would like to reiterate the main contributions of our paper and will respond to specific reviewer comments individually.

Our work contributes to our understanding of learned representations in the following ways: 

1) Our work shows that deep representations learned on large source datasets lessen, but by no means remove the problem of dataset bias; this is important as some people might and have claimed otherwise. Demonstrating performance of deep representations under domain shift is a relevant and novel contribution that paves the way for future work that will be of broad interest to the community. Additionally, we show how the novel combination of existing DA and Deep techniques allows an interesting operating point for adaptation to a new domain (or task) when too few data are available to fine tune in the conventional deep way. We show various adaptation methods are able to improve with as few as one (or none) target labels.

2) Our work shows novel experimental results on a standard domain adaptation dataset which has been extensively used in the literature. In particular, we choose to focus on the hardest shift -- amazon->webcam and augment the standard dataset by additionally considering ImageNet as a source domain. We also have practical motivations for considering webcam as a target domain since it is the most similar to a robotic vision domain.

 3) Our work shows that some layers of the representation are better for domain adaptation than others, although we do not yet propose an automatic way of selecting the 'optimal' layer.