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Found 6 papers, 3 papers with code
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation
In this paper, we propose task-specific meta distillation that simultaneously learns two models in meta-learning: a large teacher model and a small student model.
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More
Our work highlights several novel perspectives and opportunities for implementing randomized algorithms for deep learning on modern CPUs.
Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation
In this paper, we focus on Personal Fixations-based Object Segmentation (PFOS) to address issues in previous studies, such as the lack of appropriate dataset and the ambiguity in fixations-based interaction.
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes.
Attention Deep Model with Multi-Scale Deep Supervision for Person Re-Identification
Besides, most of the multi-scale models embedding the multi-scale feature learning block into the feature extraction deep network, which reduces the efficiency of inference network.
Binarized Neural Networks on the ImageNet Classification Task
We trained Binarized Neural Networks (BNNs) on the high resolution ImageNet ILSVRC-2102 dataset classification task and achieved a good performance.
