In our research,Learning Neural Network Subspaces, we take a different approach to leverage properties of the loss landscape to train more accurate models. Research shows that regions of the loss landscape areconnected by low-loss curvesin the loss landscape. Inspired byCollegial Ensembles, we train...
文章解决的问题:如何通过learning a line的方法实现比standard training更好的训练效果。 traning a line 和 standard training 定义1:connector 在connector上的平均准确率比两点的平均要好。 由文中讨论:没有linear connector fails, achiving no better accuracy than an untrained network,所以没有linear connector ...
Learning Neural Network Subspaces Mitchell Wortsman, Maxwell Horton, Carlos Guestrin, Ali Farhadi, Mohammad Rastegari Lossless Compression of Efficient Private Local Randomizers Vitaly Feldman, Kunal Talwar Private Adaptive Gradient Methods for Convex Optimization Hilal Asi, John Duchi, Alireza Fallah, Omid...
LCS: Learning Compressible Subspaces for Adaptive Network Compression at Inference Time we propose a method for training a "compressible subspace" of neural networks that contains a fine-grained spectrum of models that range from highly effici... E Nunez,M Horton,A Prabhu,... 被引量: 0发表: ...
One approach is to fit the models on different subsets of the training data, so-called bagging or random subspaces. Reply inamullah February 19, 2020 at 4:28 am # can we apply the different technique (weight initialization) approach for parameters diversity to create different models.? Repl...
the shared layer of one single neural network and compared in cosine distance. 网络解释如下: 1. Modality Invariant Subspace 减轻NIR-VIS外观差异,即想办法移除掉光谱(外观)差异,那么只剩下identity信息就容易匹配了。之前的方法都是移除一些principal subspaces,假定这些子空间是包含光谱信息的。受此启发,这里引...
(reflectance) for degradation removal. In such a way, the original space is decoupled into two smaller subspaces, expecting to be better regularized/learned. It is worth to note that our network is trained with paired images shot under different exposure conditions, instead of using any ground-...
We leverage on the premise that deep convolutional neural networks extract many redundant features to learn new subspaces for feature representation. We construct a compressed model by reconstruction from representations captured by an already trained large model. As compared to state-of-the-art, the ...
This is achieved by comparing the similarities of the principal angles between the client data subspaces spanned by those principal vectors. The approach provides a simple, yet effective clustered FL framework that addresses a broad range of data heterogeneity issues beyond simpler forms of Non-IID...
Adaptive subspaces for few-shot learning; Interventional few-shot learning; Dpgn: Distribution propagation graph network for few-shot learning; When does self-supervision improve few-shot learning?; Few-shot learning via embedding adaptation with set-to-set functions; Laplacian regularized few-...