[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。
DSD: DENSE-SPARSE-DENSE TRAINING FOR DEEP NEURAL NETWORKS,Song Han, 2017, ICLR
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 ...
中提到了DSD网络(DSD: Dense-Sparse-Dense Training for Deep Neural Networks): 本文提出一种新的训练方式,可以提升现有模型的准确率,其做法是... 3、在网络后期使用采样。保证特征图的大小。 其中1、2的目的是减少参数,同时尝试保护准确率。3是在有限的参数下最大化准确率。 论文中提出fire module: 体现了策...
1、Amount of dense pretraining upcycling的效果可能受用于初始化的dense模型的收敛情况影响,因此取了不同step的dense模型checkpoint作为upcycling的初始化,并且都继续训练了200k个step,结果如下图 结论是基本上无论从哪个checkpoint初始化MoE模型,收益都比较稳定。 2、Router type 使用不同的router(expert choice和token...
《ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT》(2022) GitHub: github.com/extreme-bert/extreme-bert《Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation》(2022) GitHub: github.com/pals-ttic/sjc...
self.model:training() for n, sample in dataloader:run() do local dataTime = dataTimer:time().real totalDataTime = totalDataTime + dataTime -- Copy input and target to the GPU self:copyInputs(sample) local output = self.model:forward(self.input) local batchSize = output:size(1) loca...
Afterwards, the training part of the data is clustered using the K-means algorithm. Finally, a copy of the trained DSD-LSTM model is fine-tuned for each obtained cluster. It helps the models predict that cluster better while they are generalizing the whole dataset quite well, which diminishes...
《DSD: Dense-Sparse-Dense Training for Neural Network》发表在ICLR17, 这是一篇关注于提升模型训练得到的准确率的文章,而不是一作传统的研究领域:模型压缩。 DSD是一种新的训练模型的方式,可以提高预训练模型的准确率。DSD和dropout不一样,虽然都是在训练过程中有prune(剪枝)操作,但是DSD是有一定依据来选择去掉...
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 ...