Zobrazeno 1 - 10
of 592
pro vyhledávání: '"Spanias, Andreas"'
Autor:
Ramirez, David F., Pujara, Deep, Tepedelenlioglu, Cihan, Srinivasan, Devarajan, Spanias, Andreas
Utility-scale solar arrays require specialized inspection methods for detecting faulty panels. Photovoltaic (PV) panel faults caused by weather, ground leakage, circuit issues, temperature, environment, age, and other damage can take many forms but o
Externí odkaz:
http://arxiv.org/abs/2407.00544
Autor:
Billingsley, Grace, Dietlmeier, Julia, Narayanaswamy, Vivek, Spanias, Andreas, OConnor, Noel E.
We propose an accurate and fast classification network for classification of brain tumors in MRI images that outperforms all lightweight methods investigated in terms of accuracy. We test our model on a challenging 2D T1-weighted CE-MRI dataset conta
Externí odkaz:
http://arxiv.org/abs/2308.00491
Mixed reality (MR) is a key technology which promises to change the future of warfare. An MR hybrid of physical outdoor environments and virtual military training will enable engagements with long distance enemies, both real and simulated. To enable
Externí odkaz:
http://arxiv.org/abs/2211.11836
Autor:
Narayanaswamy, Vivek, Mubarka, Yamen, Anirudh, Rushil, Rajan, Deepta, Spanias, Andreas, Thiagarajan, Jayaraman J.
We focus on the problem of producing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers. Motivated by the difficulty of curating suitable calibration datasets, synthetic augmentations
Externí odkaz:
http://arxiv.org/abs/2207.05286
Autor:
Iqbal, Odrika, Muro, Victor Isaac Torres, Katoch, Sameeksha, Spanias, Andreas, Jayasuriya, Suren
There is tremendous scope for improving the energy efficiency of embedded vision systems by incorporating programmable region-of-interest (ROI) readout in the image sensor design. In this work, we study how ROI programmability can be leveraged for tr
Externí odkaz:
http://arxiv.org/abs/2112.09775
Autor:
Thiagarajan, Jayaraman J., Narayanaswamy, Vivek, Rajan, Deepta, Liang, Jason, Chaudhari, Akshay, Spanias, Andreas
Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting black-box models. Commonly referred to as counterfactuals, the synthesi
Externí odkaz:
http://arxiv.org/abs/2109.14274
Unsupervised deep learning methods for solving audio restoration problems extensively rely on carefully tailored neural architectures that carry strong inductive biases for defining priors in the time or spectral domain. In this context, lot of recen
Externí odkaz:
http://arxiv.org/abs/2104.07161
With increased interest in adopting AI methods for clinical diagnosis, a vital step towards safe deployment of such tools is to ensure that the models not only produce accurate predictions but also do not generalize to data regimes where the training
Externí odkaz:
http://arxiv.org/abs/2103.03788
Through the use of carefully tailored convolutional neural network architectures, a deep image prior (DIP) can be used to obtain pre-images from latent representation encodings. Though DIP inversion has been known to be superior to conventional regul
Externí odkaz:
http://arxiv.org/abs/2010.12046