In the same way, group sparse regularization method (Scardapane, Comminiello, Hussain, & Uncini, 2017) applies the notion of ℓ2,1−norm sparsity on the sets of outgoing weights from neurons. The main drawbac
Regularization Interpretable Drawbacks Non-Symmetric Not a True Distance Metric Expensive Computational Cost 3.3. Sparse Multiclass Cross-Entropy Loss Sparse Multiclass Cross-Entropy Loss, often referred to as Sparse Categorical Cross-Entropy Loss, is a loss function commonly used in multi-class classific...
(2016). Group Sparse Regularization for Deep Neural Networks. arXiv preprint arXiv:1607.00485.S. Scardapane, D. Comminiello, A. Hussain, and A. Uncini. Group sparse regularization for deep neural networks. Neu- rocomputing, 241:81-89, 2017. 8...
2020 年 Baidu 的论文《Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems》 中的数据,凤巢线上模型的 Sparse 参数已经达到 10TB Baidu 蜂巢的 CTR 模型 2021 年Facebook Instagram 的个性化推荐模型参数也达到了 10TB Today, constantly improving recommendations for the sheer...
Group Sparse Regularization for Deep Neural Networks Scardapane S, Comminiello D, Hussain A, et al 摘要: 在本论文中,我们考虑同时进行以下优化任务:深度神经网络的权重、隐层神经元的数量以及输入特征选择。虽然这三个问题通常被分开处理,但我们提出了一个简单的正则化公式,能够在标准的优化历程下并行地解决...
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization 2020 NeurIPS PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning 2020 ECCV Topology-Preserving Class-Incremental Learning 2020 ECCV Uncertainty-based Continual Learning with Adaptive Regularization 2019 NeurIPS...
"Learning Multiple Adverse Weather Removal via Two-Stage Knowledge Learning and Multi-Contrastive Regularization: Toward a Unified Model." CVPR. 2022. ⭐⭐⭐⭐ Nighttime & Low-light dehaze + nighttime: Yan, Wending, Robby T. Tan, and Dengxin Dai. "Nighttime defogging using high-low ...
Methods based on sparse ground truth To strengthen the supervised signals, the sparse ground truth is widely incorporated into the training framework. Kuznietsov et al. [48] adopted the ground truth depth collected by LIDAR for semi-supervised learning. Besides, both the left and right depth map...
Naturally, if more intensity maps are recorded by the sensor, there will be more prior knowledge for regularization, further reducing the ill-posedness of the problem. By moving the sensor axially, the intensity maps of different defocus distances are recorded as an intensity constraint, and then...
GRSL_BFE_MA -> Deep Learning-based Building Footprint Extraction with Missing Annotations using a novel loss function FER-CNN -> Detection, Classification and Boundary Regularization of Buildings in Satellite Imagery Using Faster Edge Region Convolutional Neural Networks UNET-Image-Segmentation-Satellite-...