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20172022
most citedFine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization

15 citations · 49 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.CV2021

Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation

Mengyao Zhai, Lei Chen, Jiawei He +3

Humans accumulate knowledge in a lifelong fashion. Modern deep neural networks, on the other hand, are susceptible to catastrophic forgetting: when adapted to perform new tasks, th…

cs.CV20211 cited

Learning Discriminative Prototypes with Dynamic Time Warping

Xiaobin Chang, Frederick Tung, Greg Mori

Dynamic Time Warping (DTW) is widely used for temporal data processing. However, existing methods can neither learn the discriminative prototypes of different classes nor exploit s…

cs.CV2019

Lifelong GAN: Continual Learning for Conditional Image Generation

Mengyao Zhai, Lei Chen, Fred Tung +3

Lifelong learning is challenging for deep neural networks due to their susceptibility to catastrophic forgetting. Catastrophic forgetting occurs when a trained network is not able…

cs.CV2019

Similarity-Preserving Knowledge Distillation

Frederick Tung, Greg Mori

Knowledge distillation is a widely applicable technique for training a student neural network under the guidance of a trained teacher network. For example, in neural network compre…

cs.CV201714 cited

Backtracking Regression Forests for Accurate Camera Relocalization

Lili Meng, Jianhui Chen, Frederick Tung +3

Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection. Recent…

cs.CV201715 cited

Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization

Frederick Tung, Srikanth Muralidharan, Greg Mori

When approaching a novel visual recognition problem in a specialized image domain, a common strategy is to start with a pre-trained deep neural network and fine-tune it to the spec…