[论文解读] DSD -- Dense-Sparse-Dense Training for Neural Network,程序员大本营,技术文章内容聚合第一站。
In the first D (Dense) step, we train a dense network to learn connection weights and importance. In the S (Sparse) step, we regularize the network by pruning the unimportant connections with small weights and retraining the network given the sparsity constraint. In the final D (re-Dense)...
We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks and achieving better optimization performance. In the first D (Dense) step, we train a dense network to learn connection weights and importance. In the S (Sparse) step, we regularize the network by ...
We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks and achieving better optimization performance. In the first D (Dense) step, we train a dense network to learn connection weights and importance. In the S (Sparse) step, we regularize the network by ...
Dense-Sparse-Dense(DSD)训练法 不得不说一下原作的这个小发现,使用裁剪之后的模型为初始值,再次进行训练调优所有参数,正确率能够提升4.3%。 稀疏相当于一种正则化,有机会把解从局部极小中解放出来。这种...梦寐以求的事——压缩神经网络参数。但和以往不同,原作不是在前人网络基础上修修补补(例如Deep Compressio...
对比《Scaling language models: Methods, analysis & insights from training gopher》中的depth tiling(dense upcycling) 和 sparse upcycling的预训练效果,结果当然是sparse upcycling效率更高点,如下图所示 (不过这里没有提及depth tiling之后的模型规模) 2.2.消融实验 1、Amount of dense pretraining upcycling的效果...
几篇论文实现代码:《Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection》(NeurIPS 2022) GitHub: github.com/stevewongv/Sparse2Dense [fig6] 《Redeeming Intrinsic Rewards via Constr...
结合少量的距离信息和彩色信息进行距离图像预测 ICRA 2018 "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" (Torch Implementation) - Ewenwan/sparse-to-dense
沿着目标检测领域中Dense和Dense-to-Sparse的框架,Sparse R-CNN建立了一种彻底的Sparse框架, 脱离anchor box,reference point,Region Proposal Network(RPN)等概念,无需Non-Maximum Suppression(NMS)后处理, 在标准的COCO benchmark上使用ResNet-50 FPN单模型在标准3x training schedule达到了44.5 AP和 22 FPS。
The results show that the proposed sparse matrix multipliers can outperform dense multipliers when sparsity levels are higher than 70% and the improvements are more evident when higher precision arithmetic or structural pruning is used. Additionally, sparsity levels as high as 99% can maintain the ...