4 citations · 12 across the 7 of their papers we have counts for
6 papers · 1 filter
Differentiable Transportation Pruning
Yunqiang Li, Jan C. van Gemert, Torsten Hoefler +3
Deep learning algorithms are increasingly employed at the edge. However, edge devices are resource constrained and thus require efficient deployment of deep neural networks. Prunin…
Understanding weight-magnitude hyperparameters in training binary networks
Joris Quist, Yunqiang Li, Jan van Gemert
Binary Neural Networks (BNNs) are compact and efficient by using binary weights instead of real-valued weights. Current BNNs use latent real-valued weights during training, where s…
Deep Unsupervised Image Hashing by Maximizing Bit Entropy
Yunqiang Li, Jan van Gemert
Unsupervised hashing is important for indexing huge image or video collections without having expensive annotations available. Hashing aims to learn short binary codes for compact…
Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels
Xiangwei Shi, Seyran Khademi, Yunqiang Li +1
Current weakly supervised object localization and segmentation rely on class-discriminative visualization techniques to generate pseudo-labels for pixel-level training. Such visual…
WeightAlign: Normalizing Activations by Weight Alignment
Xiangwei Shi, Yunqiang Li, Xin Liu +1
Batch normalization (BN) allows training very deep networks by normalizing activations by mini-batch sample statistics which renders BN unstable for small batch sizes. Current smal…
Push for Quantization: Deep Fisher Hashing
Yunqiang Li, Wenjie Pei, Yufei zha +1
Current massive datasets demand light-weight access for analysis. Discrete hashing methods are thus beneficial because they map high-dimensional data to compact binary codes that a…