Search Results for author:
Found 11 papers, 2 papers with code
Salient Object-Aware Background Generation using Text-Guided Diffusion Models
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.
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space
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.
Runtime Deep Model Multiplexing for Reduced Latency and Energy Consumption Inference
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.
Video Person Re-ID: Fantastic Techniques and Where to Find Them
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
Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification
Small inter-class and large intra-class variations are the main challenges in fine-grained visual classification.
BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services
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.
Towards Collaborative Intelligence Friendly Architectures for Deep Learning
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
Gradient Agreement as an Optimization Objective for Meta-Learning
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.
A Meta-Learning Approach for Custom Model Training
Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks.
JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services
Deep learning models are being deployed in many mobile intelligent applications.
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA
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.
