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20142023
most citedRole of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data

23 citations · 35 across the 10 of their papers we have counts for

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6 papers · 1 filter

cs.CV20238 cited

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…

cs.CV20222 cited

Domain Alignment Meets Fully Test-Time Adaptation

Kowshik Thopalli, Pavan Turaga, Jayaraman J. Thiagarajan

A foundational requirement of a deployed ML model is to generalize to data drawn from a testing distribution that is different from training. A popular solution to this problem is…

cs.CV2016

Diversity Promoting Online Sampling for Streaming Video Summarization

Rushil Anirudh, Ahnaf Masroor, Pavan Turaga

Many applications benefit from sampling algorithms where a small number of well chosen samples are used to generalize different properties of a large dataset. In this paper, we use…

cs.CV2015

Reconstruction-free action inference from compressive imagers

Kuldeep Kulkarni, Pavan Turaga

Persistent surveillance from camera networks, such as at parking lots, UAVs, etc., often results in large amounts of video data, resulting in significant challenges for inference i…

cs.CV20142 cited

Interactively Test Driving an Object Detector: Estimating Performance on Unlabeled Data

Rushil Anirudh, Pavan Turaga

In this paper, we study the problem of `test-driving' a detector, i.e. allowing a human user to get a quick sense of how well the detector generalizes to their specific requirement…

cs.CV2014

Geometry-based Adaptive Symbolic Approximation for Fast Sequence Matching on Manifolds

Rushil Anirudh, Pavan Turaga

In this paper, we consider the problem of fast and efficient indexing techniques for sequences evolving in non-Euclidean spaces. This problem has several applications in the areas…