no code implementations • 24 Feb 2023 • Debasmit Das, Shubhankar Borse, Hyojin Park, Kambiz Azarian, Hong Cai, Risheek Garrepalli, Fatih Porikli
Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained image semantic segmentation model to unlabeled batches of target domain test images, different from real-world, where samples arrive one-by-one in an online fashion.
no code implementations • 12 Dec 2022 • Kambiz Azarian, Debasmit Das, Hyojin Park, Fatih Porikli
In this approach, we do not assume test-time access to the labeled source dataset.
no code implementations • 23 Apr 2021 • Mohammad Samragh, Hossein Hosseini, Aleksei Triastcyn, Kambiz Azarian, Joseph Soriaga, Farinaz Koushanfar
In our method, the edge device runs the model up to a split layer determined based on its computational capacity.
no code implementations • 19 Mar 2021 • Kambiz Azarian, Fatih Porikli
We report, for the first time, on the cascade weight shedding phenomenon in deep neural networks where in response to pruning a small percentage of a network's weights, a large percentage of the remaining is shed over a few epochs during the ensuing fine-tuning phase.
no code implementations • 1 Jan 2021 • Mohammad Samragh, Hossein Hosseini, Kambiz Azarian, Joseph Soriaga
Splitting network computations between the edge device and the cloud server is a promising approach for enabling low edge-compute and private inference of neural networks.
no code implementations • 28 Feb 2020 • Kambiz Azarian, Yash Bhalgat, Jinwon Lee, Tijmen Blankevoort
This is in contrast to other methods that search for per-layer thresholds via a computationally intensive iterative pruning and fine-tuning process.