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11/01/2022
Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation
Methods for improving the efficiency of deep network training (i.e. the ...
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08/07/2022
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation
Optical flow estimation in omnidirectional videos faces two significant ...
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07/13/2022
Lipschitz Continuity Retained Binary Neural Network
Relying on the premise that the performance of a binary neural network c...
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07/06/2022
Network Binarization via Contrastive Learning
Neural network binarization accelerates deep models by quantizing their ...
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01/30/2022
Win the Lottery Ticket via Fourier Analysis: Frequencies Guided Network Pruning
With the remarkable success of deep learning recently, efficient network...
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10/08/2021
Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks
Algorithmic bias is of increasing concern, both to the research communit...
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08/29/2021
Lipschitz Continuity Guided Knowledge Distillation
Knowledge distillation has become one of the most important model compre...
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06/15/2021
Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation
In recent years the ubiquitous deployment of AI has posed great concerns...
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12/05/2020
Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression
Deep neural networks (DNNs) have been extremely successful in solving ma...
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10/15/2020
Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset
Recently, there has been a growing interest in wearable sensors which pr...
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10/15/2020
Revisiting Optical Flow Estimation in 360 Videos
Nowadays 360 video analysis has become a significant research topic in t...
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08/14/2020
