@article{chen_dutton_ramachandra_wu_vatsavai_2021, title={Local Clustering with Mean Teacher for Semi-supervised learning}, ISSN={["1051-4651"]}, DOI={10.1109/ICPR48806.2021.9412469}, abstractNote={The Mean Teacher (MT) model of Tarvainen and Valpola has shown good performance on several semi-supervised benchmark datasets. MT maintains a teacher model's weights as the exponential moving average of a student model's weights and minimizes the divergence between their probability predictions under diverse perturbations of the inputs. However, MT is known to suffer from confirmation bias, that is, reinforcing incorrect teacher model predictions. In this work, we propose a simple yet effective method called Local Clustering (LC) to mitigate the effect of confirmation bias. In MT, each data point is considered independent of other points during training; however, data points are likely to be close to each other in feature space if they share similar features. Motivated by this, we cluster data points locally by minimizing the pairwise distance between neighboring data points in feature space. Combined with a standard classification cross-entropy objective on labeled data points, the misclassified unlabeled data points are pulled towards high-density regions of their correct class with the help of their neighbors, thus improving model performance. We demonstrate on semi-supervised benchmark datasets SVHN and CIFAR-10 that adding our LC loss to MT yields significant improvements compared to MT and performance comparable to the state of the art in semi-supervised learning11The code is available at: https://github.com/jay1204/local_clustering_with_mt_for_ssl.}, journal={2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)}, author={Chen, Zexi and Dutton, Benjamin and Ramachandra, Bharathkumar and Wu, Tianfu and Vatsavai, Ranga Raju}, year={2021}, pages={6243–6250} }
 @inproceedings{chen_ramachandra_vatsavai_2017, title={Hierarchical change detection framework for biomass monitoring}, DOI={10.1109/igarss.2017.8127030}, abstractNote={In this paper, we present a nearest neighbor based hierarchical change detection methodology for analyzing multi-temporal remote sensing imagery. A key contribution of this work is to define change as hierarchical rather than boolean. Based on this definition of change pattern, we developed a novel time series similarity based change detection framework for identifying inter-annual changes by exploiting phenological properties of growing crops from satellite time series imagery. The proposed framework consists of four components: hierarchical clustering tree construction, nearest neighbor based classification, relaxation labeling, and change detection using similarity hierarchy. Though the proposed approach is unsupervised, we present evaluation using manually induced change regions embedded in the real dataset. We compare our method with the widely used K-Means clustering and evaluation shows that K-Means over-detects changes in comparison to our proposed method.}, booktitle={2017 ieee international geoscience and remote sensing symposium (igarss)}, author={Chen, Z. and Ramachandra, B. and Vatsavai, Ranga Raju}, year={2017}, pages={620–623} }
 @inproceedings{chen_vatsavai_ramachandra_zhang_singh_sukumar_2016, title={Scalable nearest neighbor based hierarchical change detection framework for crop monitoring}, DOI={10.1109/bigdata.2016.7840735}, abstractNote={Monitoring biomass over large geographic regions for changes in vegetation and cropping patterns is important for many applications. Changes in vegetation happen due to reasons ranging from climate change and damages to new government policies and regulations. Remote sensing imagery (multi-spectral and multi-temporal) is widely used in change pattern mapping studies. Existing bi-temporal change detection techniques are better suited for multi-spectral images and time series based techniques are more suited for analyzing multi-temporal images. A key contribution of this work is to define change as hierarchical rather than boolean. Based on this definition of change pattern, we developed a novel time series similarity based change detection framework for identifying inter-annual changes by exploiting phenological properties of growing crops from satellite time series imagery. The proposed framework consists of three components: hierarchical clustering tree construction, nearest neighbor based classification, and change detection using similarity hierarchy. Though the proposed approach is unsupervised, we present evaluation using manually induced change regions embedded in the real dataset. We compare our method with the widely used K-Means clustering and evaluation shows that K-Means over-detects changes in comparison to our proposed method.}, booktitle={2016 IEEE International Conference on Big Data (Big Data)}, author={Chen, Z. X. and Vatsavai, Ranga Raju and Ramachandra, B. and Zhang, Q. and Singh, N. and Sukumar, S.}, year={2016}, pages={1309–1314} }