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]:
The submission tackles the problem of one-shot classifier adaptation between biased datasets that contain overlapping object categories. The authors design a number of experiments to evaluate known transfer/adaptation approaches on deep convnet features taken from the last 3 layers of the Krizhevsky network. The basic approach is that the convnet is trained on LSVRC 1000-category data, then 16 categories are chosen that overlap with categories in 2 other datasets (amazon and webcam). Feature representations are taken from one of the layers of the network using amazon or imagenet data and webcam data, and adaptive classifiers are tested using the amazon or imagenet source and a single webcam image as target.

There is little that is innovative in the submission, since it uses only published or trivial approaches for the convnet and the domain adaptation and the empirical results are not broad enough. Moreover, the work does not contribute to our understanding of learned representations, so I see little relevance for ICLR. 

The paper is well-written and offers a number of intuitive explanations for the results, although some of the conclusions don't seem well-justified given the limited evidence from only one target domain. The authors identify an interesting next step of doing learning in the convnet layers using feedback through the adaptation classifier, which could be worthwhile.