1 code implementation • 15 Apr 2024 • Amir Erfan Eshratifar, Joao V. B. Soares, Kapil Thadani, Shaunak Mishra, Mikhail Kuznetsov, Yueh-Ning Ku, Paloma de Juan
Generating background scenes for salient objects plays a crucial role across various domains including creative design and e-commerce, as it enhances the presentation and context of subjects by integrating them into tailored environments.
no code implementations • 12 Feb 2020 • Mohammad Saeed Abrishami, Amir Erfan Eshratifar, David Eigen, Yanzhi Wang, Shahin Nazarian, Massoud Pedram
However, fine-tuning a transfer model with data augmentation in the raw input space has a high computational cost to run the full network for every augmented input.
no code implementations • 14 Jan 2020 • Amir Erfan Eshratifar, Massoud Pedram
The proposed algorithm allows the mobile device to detect the inputs that can be processed locally and the ones that require a larger model and should be sent a cloud server.
1 code implementation • 21 Nov 2019 • Priyank Pathak, Amir Erfan Eshratifar, Michael Gormish
The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest.
Ranked #1 on Person Re-Identification on MARS
no code implementations • 6 Sep 2019 • Amir Erfan Eshratifar, David Eigen, Michael Gormish, Massoud Pedram
Small inter-class and large intra-class variations are the main challenges in fine-grained visual classification.
no code implementations • 4 Feb 2019 • Amir Erfan Eshratifar, Amirhossein Esmaili, Massoud Pedram
Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud.
no code implementations • 1 Feb 2019 • Amir Erfan Eshratifar, Amirhossein Esmaili, Massoud Pedram
In this approach, referred to as collaborative intelligence, intermediate features computed on the mobile device are offloaded to the cloud instead of the raw input data of the network, reducing the size of the data needed to be sent to the cloud.
Distributed, Parallel, and Cluster Computing
no code implementations • 18 Oct 2018 • Amir Erfan Eshratifar, David Eigen, Massoud Pedram
Therefore, the degree of the contribution of a task to the parameter updates is controlled by introducing a set of weights on the loss function of the tasks.
no code implementations • 21 Sep 2018 • Amir Erfan Eshratifar, Mohammad Saeed Abrishami, David Eigen, Massoud Pedram
Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks.
no code implementations • 25 Jan 2018 • Amir Erfan Eshratifar, Mohammad Saeed Abrishami, Massoud Pedram
Deep learning models are being deployed in many mobile intelligent applications.
no code implementations • 11 Jan 2018 • Mahdi Nazemi, Amir Erfan Eshratifar, Massoud Pedram
With ever-increasing application of machine learning models in various domains such as image classification, speech recognition and synthesis, and health care, designing efficient hardware for these models has gained a lot of popularity.