23 citations · 45 across the 12 of their papers we have counts for
12 papers
Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation
Eun Som Jeon, Jisoo Lee, Huisu Lim +3
Wearable sensor-based human gait analysis holds great promise in healthcare, rehabilitation, clinical diagnosis and monitoring, and sports activities. Specifically, ground reaction…
Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Eun Som Jeon, Sinjini Mitra, Jisoo Lee +4
Human gait analysis with wearable sensors has been widely used in various applications, such as daily life healthcare, rehabilitation, physical therapy, and clinical diagnostics an…
Intra-class Patch Swap for Self-Distillation
Hongjun Choi, Eun Som Jeon, Ankita Shukla +1
Knowledge distillation (KD) is a valuable technique for compressing large deep learning models into smaller, edge-suitable networks. However, conventional KD frameworks rely on pre…
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data
Eun Som Jeon, Hongjun Choi, Matthew P. Buman +1
The analysis of wearable sensor data has enabled many successes in several applications. To represent the high-sampling rate time-series with sufficient detail, the use of topologi…
Deep Geometric Moments Promote Shape Consistency in Text-to-3D Generation
Utkarsh Nath, Rajeev Goel, Eun Som Jeon +5
To address the data scarcity associated with 3D assets, 2D-lifting techniques such as Score Distillation Sampling (SDS) have become a widely adopted practice in text-to-3D generati…
Leveraging Topological Guidance for Improved Knowledge Distillation
Eun Som Jeon, Rahul Khurana, Aishani Pathak +1
Deep learning has shown its efficacy in extracting useful features to solve various computer vision tasks. However, when the structure of the data is complex and noisy, capturing e…
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data
Eun Som Jeon, Hongjun Choi, Ankita Shukla +4
Deep learning methods have achieved a lot of success in various applications involving converting wearable sensor data to actionable health insights. A common application areas is…
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks
Jinyung Hong, Eun Som Jeon, Changhoon Kim +5
Biased attributes, spuriously correlated with target labels in a dataset, can problematically lead to neural networks that learn improper shortcuts for classifications and limit th…
Leveraging Angular Distributions for Improved Knowledge Distillation
Eun Som Jeon, Hongjun Choi, Ankita Shukla +1
Knowledge distillation as a broad class of methods has led to the development of lightweight and memory efficient models, using a pre-trained model with a large capacity (teacher n…
Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study
Hongjun Choi, Eun Som Jeon, Ankita Shukla +1
Mixup is a popular data augmentation technique based on creating new samples by linear interpolation between two given data samples, to improve both the generalization and robustne…
A Style-aware Discriminator for Controllable Image Translation
Kunhee Kim, Sanghun Park, Eunyeong Jeon +2
Current image-to-image translations do not control the output domain beyond the classes used during training, nor do they interpolate between different domains well, leading to imp…
Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data
Eun Som Jeon, Anirudh Som, Ankita Shukla +3
Deep neural networks are parametrized by several thousands or millions of parameters, and have shown tremendous success in many classification problems. However, the large number o…