4 citations · 10 across the 11 of their papers we have counts for
11 papers
Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation
Cody Blakeney, Jessica Zosa Forde, Jonathan Frankle +2
Methods for improving the efficiency of deep network training (i.e. the resources required to achieve a given level of model quality) are of immediate benefit to deep learning prac…
Lipschitz Continuity Retained Binary Neural Network
Yuzhang Shang, Dan Xu, Bin Duan +3
Relying on the premise that the performance of a binary neural network can be largely restored with eliminated quantization error between full-precision weight vectors and their co…
Network Binarization via Contrastive Learning
Yuzhang Shang, Dan Xu, Ziliang Zong +2
Neural network binarization accelerates deep models by quantizing their weights and activations into 1-bit. However, there is still a huge performance gap between Binary Neural Net…
Win the Lottery Ticket via Fourier Analysis: Frequencies Guided Network Pruning
Yuzhang Shang, Bin Duan, Ziliang Zong +2
With the remarkable success of deep learning recently, efficient network compression algorithms are urgently demanded for releasing the potential computational power of edge device…
Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks
Cody Blakeney, Gentry Atkinson, Nathaniel Huish +3
Algorithmic bias is of increasing concern, both to the research community, and society at large. Bias in AI is more abstract and unintuitive than traditional forms of discriminatio…
Lipschitz Continuity Guided Knowledge Distillation
Yuzhang Shang, Bin Duan, Ziliang Zong +2
Knowledge distillation has become one of the most important model compression techniques by distilling knowledge from larger teacher networks to smaller student ones. Although grea…
Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation
Cody Blakeney, Nathaniel Huish, Yan Yan +1
In recent years the ubiquitous deployment of AI has posed great concerns in regards to algorithmic bias, discrimination, and fairness. Compared to traditional forms of bias or disc…
Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression
Cody Blakeney, Xiaomin Li, Yan Yan +1
Deep neural networks (DNNs) have been extremely successful in solving many challenging AI tasks in natural language processing, speech recognition, and computer vision nowadays. Ho…
Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset
Keshav Bhandari, Mario A. DeLaGarza, Ziliang Zong +2
Recently, there has been a growing interest in wearable sensors which provides new research perspectives for 360 ° video analysis. However, the lack of 360 ° datasets in literature…
Revisiting Optical Flow Estimation in 360 Videos
Keshav Bhandari, Ziliang Zong, Yan Yan
Nowadays 360 video analysis has become a significant research topic in the field since the appearance of high-quality and low-cost 360 wearable devices. In this paper, we propose a…
Audio-Visual Event Localization via Recursive Fusion by Joint Co-Attention
Bin Duan, Hao Tang, Wei Wang +3
The major challenge in audio-visual event localization task lies in how to fuse information from multiple modalities effectively. Recent works have shown that attention mechanism i…