no code implementations • 2 Dec 2023 • Renan A. Rojas-Gomez, Karan Singhal, Ali Etemad, Alex Bijamov, Warren R. Morningstar, Philip Andrew Mansfield
Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images.
no code implementations • 7 Nov 2023 • Philip Andrew Mansfield, Arash Afkanpour, Warren Richard Morningstar, Karan Singhal
In this work, we propose a new family of local transformations based on Gaussian random fields to generate image augmentations for self-supervised representation learning.
no code implementations • 11 Sep 2023 • Pengfei Guo, Warren Richard Morningstar, Raviteja Vemulapalli, Karan Singhal, Vishal M. Patel, Philip Andrew Mansfield
To mitigate this issue and facilitate training of large models on edge devices, we introduce a simple yet effective strategy, Federated Layer-wise Learning, to simultaneously reduce per-client memory, computation, and communication costs.
no code implementations • 23 May 2023 • Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar
We then approximate this process using Variational Inference to train our model efficiently.
no code implementations • 30 Sep 2022 • Raviteja Vemulapalli, Warren Richard Morningstar, Philip Andrew Mansfield, Hubert Eichner, Karan Singhal, Arash Afkanpour, Bradley Green
In this work, we focus on federated training of dual encoding models on decentralized data composed of many small, non-IID (independent and identically distributed) client datasets.
no code implementations • 15 Apr 2021 • Yu-Chuan Su, Raviteja Vemulapalli, Ben Weiss, Chun-Te Chu, Philip Andrew Mansfield, Lior Shapira, Colvin Pitts
To address this issue, we propose a deep learning-based approach that provides suggestions to the photographer on how to adjust the camera view before capturing.
no code implementations • ICCV 2021 • Xiangyun Zhao, Raviteja Vemulapalli, Philip Andrew Mansfield, Boqing Gong, Bradley Green, Lior Shapira, Ying Wu
While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled training data, their performance drops significantly as the amount of labeled data decreases.
1 code implementation • 30 Jan 2018 • Philip Andrew Mansfield, Quan Wang, Carlton Downey, Li Wan, Ignacio Lopez Moreno
We present a novel algorithm, called Links, designed to perform online clustering on unit vectors in a high-dimensional Euclidean space.
4 code implementations • 28 Oct 2017 • Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno
For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications.
Ranked #2 on Speaker Diarization on CALLHOME-109