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ORCIDAmir Erfan Eshratifar
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2020 – today
- 2024
[c9]Amir Erfan Eshratifar, João V. B. Soares, Kapil Thadani, Shaunak Mishra, Mikhail Kuznetsov, Yueh-Ning Ku, Paloma de Juan:
Salient Object-Aware Background Generation using Text-Guided Diffusion Models. CVPR Workshops 2024: 7489-7499- [i11]Amir Erfan Eshratifar, João V. B. Soares, Kapil Thadani, Shaunak Mishra, Mikhail Kuznetsov, Yueh-Ning Ku, Paloma de Juan:
Salient Object-Aware Background Generation using Text-Guided Diffusion Models. CoRR abs/2404.10157 (2024) - 2021
- [j2]Amir Erfan Eshratifar
, David Eigen, Michael Gormish, Massoud Pedram:
Coarse2Fine: a two-stage training method for fine-grained visual classification. Mach. Vis. Appl. 32(2): 49 (2021) - [j1]Amir Erfan Eshratifar, Mohammad Saeed Abrishami, Massoud Pedram:
JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services. IEEE Trans. Mob. Comput. 20(2): 565-576 (2021) - 2020
- [c8]Priyank Pathak, Amir Erfan Eshratifar, Michael Gormish:
Video Person Re-ID: Fantastic Techniques and Where to Find Them (Student Abstract). AAAI 2020: 13893-13894 - [c7]Amir Erfan Eshratifar, Massoud Pedram:
Runtime Deep Model Multiplexing for Reduced Latency and Energy Consumption Inference. ICCD 2020: 263-270 - [c6]Mohammad Saeed Abrishami, Amir Erfan Eshratifar, David Eigen, Yanzhi Wang, Shahin Nazarian, Massoud Pedram:
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space. ISQED 2020: 347-351 - [i10]Amir Erfan Eshratifar, Massoud Pedram:
Run-time Deep Model Multiplexing. CoRR abs/2001.05870 (2020) - [i9]Mohammad Saeed Abrishami, Amir Erfan Eshratifar, David Eigen, Yanzhi Wang, Shahin Nazarian, Massoud Pedram:
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space. CoRR abs/2002.04776 (2020)
2010 – 2019
- 2019
- [c5]Amir Erfan Eshratifar, Mohammad Saeed Abrishami, David Eigen, Massoud Pedram:
A Meta-Learning Approach for Custom Model Training. AAAI 2019: 9937-9938 - [c4]Amir Erfan Eshratifar, Amirhossein Esmaili, Massoud Pedram:
BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services. ISLPED 2019: 1-6 - [c3]Amir Erfan Eshratifar, Amirhossein Esmaili, Massoud Pedram:
Towards Collaborative Intelligence Friendly Architectures for Deep Learning. ISQED 2019: 14-19 - [i8]Amir Erfan Eshratifar, Amirhossein Esmaili, Massoud Pedram:
Towards Collaborative Intelligence Friendly Architectures for Deep Learning. CoRR abs/1902.00147 (2019) - [i7]Amir Erfan Eshratifar, Amirhossein Esmaili, Massoud Pedram:
BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services. CoRR abs/1902.01000 (2019) - [i6]Amir Erfan Eshratifar, David Eigen, Michael Gormish, Massoud Pedram:
Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification. CoRR abs/1909.02680 (2019) - [i5]Priyank Pathak, Amir Erfan Eshratifar, Michael Gormish:
Video Person Re-ID: Fantastic Techniques and Where to Find Them. CoRR abs/1912.05295 (2019) - 2018
- [c2]Amir Erfan Eshratifar, Massoud Pedram:
Energy and Performance Efficient Computation Offloading for Deep Neural Networks in a Mobile Cloud Computing Environment. ACM Great Lakes Symposium on VLSI 2018: 111-116 - [c1]Mahdi Nazemi, Amir Erfan Eshratifar, Massoud Pedram:
A hardware-friendly algorithm for scalable training and deployment of dimensionality reduction models on FPGA. ISQED 2018: 395-400 - [i4]Mahdi Nazemi, Amir Erfan Eshratifar, Massoud Pedram:
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA. CoRR abs/1801.04014 (2018) - [i3]Amir Erfan Eshratifar, Mohammad Saeed Abrishami, Massoud Pedram:
JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services. CoRR abs/1801.08618 (2018) - [i2]Amir Erfan Eshratifar, Mohammad Saeed Abrishami, David Eigen, Massoud Pedram:
A Meta-Learning Approach for Custom Model Training. CoRR abs/1809.08346 (2018) - [i1]Amir Erfan Eshratifar, David Eigen, Massoud Pedram:
Gradient Agreement as an Optimization Objective for Meta-Learning. CoRR abs/1810.08178 (2018)
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