913 citations · 2.7k across the 51 of their papers we have counts for
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Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
Hongxu Yin, Pavlo Molchanov, Zhizhong Li +5
We introduce DeepInversion, a new method for synthesizing images from the image distribution used to train a deep neural network. We 'invert' a trained network (teacher) to synthes…
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo
Jiayu Yang, Wei Mao, Jose M. Alvarez +1
We propose a cost volume-based neural network for depth inference from multi-view images. We demonstrate that building a cost volume pyramid in a coarse-to-fine manner instead of c…
VACL: Variance-Aware Cross-Layer Regularization for Pruning Deep Residual Networks
Shuang Gao, Xin Liu, Lung-Sheng Chien +2
Improving weight sparsity is a common strategy for producing light-weight deep neural networks. However, pruning models with residual learning is more challenging. In this paper, w…
Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions
Kashyap Chitta, Jose M. Alvarez, Martial Hebert
Semantic segmentation with Convolutional Neural Networks is a memory-intensive task due to the high spatial resolution of feature maps and output predictions. In this paper, we pre…
Training Data Subset Search with Ensemble Active Learning
Kashyap Chitta, Jose M. Alvarez, Elmar Haussmann +1
Deep Neural Networks (DNNs) often rely on very large datasets for training. Given the large size of such datasets, it is conceivable that they contain certain samples that either d…