Search Results for author:
Found 7 papers, 2 papers with code
Neural Network Pruning with Residual-Connections and Limited-Data
Knowledge distillation is an effective approach to compensate for the weakness of limited data.
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval
Moreover, on general image retrieval datasets, SCDA achieves comparable retrieval results with state-of-the-art general image retrieval approaches.
AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference
Previous filter pruning algorithms regard channel pruning and model fine-tuning as two independent steps.
Learning Effective Binary Visual Representations with Deep Networks
Although traditionally binary visual representations are mainly designed to reduce computational and storage costs in the image retrieval research, this paper argues that binary visual representations can be applied to large scale recognition and detection problems in addition to hashing in retrieval.
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Similar experiments with ResNet-50 reveal that even for a compact network, ThiNet can also reduce more than half of the parameters and FLOPs, at the cost of roughly 1$\%$ top-5 accuracy drop.
An Entropy-based Pruning Method for CNN Compression
Experiments on the ILSVRC-12 benchmark demonstrate the effectiveness of our method.
Dense CNN Learning with Equivalent Mappings
Large receptive field and dense prediction are both important for achieving high accuracy in pixel labeling tasks such as semantic segmentation.
