Kernel Methods for Unsupervised Domain Adaptation by Boqing Gong
نویسنده
چکیده
xi
منابع مشابه
Overcoming Dataset Bias: An Unsupervised Domain Adaptation Approach
Recent studies have shown that recognition datasets are biased. Paying no heed to those biases, learning algorithms often result in classifiers with poor crossdataset generalization. We are developing domain adaptation techniques to overcome those biases and yield classifiers with significantly improved performance when generalized to new testing datasets. Our work enables us to continue to har...
متن کاملConnecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation
Learning domain-invariant features is of vital importance to unsupervised domain adaptation, where classifiers trained on the source domain need to be adapted to a different target domain for which no labeled examples are available. In this paper, we propose a novel approach for learning such features. The central idea is to exploit the existence of landmarks, which are a subset of labeled data...
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
متن کاملLarge-Margin Determinantal Point Processes
Investigate determinantal point processes (DPPs) for discriminative subset selection Proposemargin based parameter estimation to explicitly track errors in selecting subsets Balance different types of evaluation metrics, e.g., precision and recall Improve modeling flexibility by multiple-kernel based parameterization Attain state-of-the-art performance on the tasks of video and docume...
متن کاملReshaping Visual Datasets for Domain Adaptation
In visual recognition problems, the common data distribution mismatches between training and testing make domain adaptation essential. However, image data is difficult to manually divide into the discrete domains required by adaptation algorithms, and the standard practice of equating datasets with domains is a weak proxy for all the real conditions that alter the statistics in complex ways (li...
متن کامل