持续学习(ContinualLearning)是指一个学习系统能够不断地从新样本中学习新的知识,并且保 存大部分已经学习到的知识,其学习过程也十分类 似于人类自身的学习模式。 困难点:但是持续学习需要面对 一个非常重要的挑战是灾难性遗忘,即需要平衡新知识与旧知识之间的关系。 知识蒸馏能够将已学习的知识传递给学习模型 实现“...
A: 这篇论文试图解决知识蒸馏(Knowledge Distillation,简称KD)中确定最优知识融合比例的问题。知识蒸馏的目标是将一个大型教师网络的知识转移到一个较小的学生网络中。在训练过程中,学生网络受到来自教师网络的软监督信号(教师预测)和来自真实标签的硬监督信号(ground truth)的影响。然而,确定一个平衡这两种信号的最优...
Inspired by pseudo-rehearsal and regularization methods, we propose a novel Continual Learning Based on Knowledge Distillation and Representation Learning (KRCL) model, which employs Beta-VAE as a representation learning module to extract a shared representation of learned tasks. In addition, Beta-VAE ...
inferenceknowledge-distillationdistillationstable-diffusion UpdatedOct 18, 2023 Python An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning deep-learningpytorchknowledge-distillationdercontinual-learningexperience-replayneurips2020...
old model. For continual learning on model\({t}+1\), EPicker only distills knowledge on the exemplar\(({E}_{1}\ldots {E}_{t})\)and does not involve that on the new dataset\({D}_{t+1}\). We use\({L}_{2}\)loss for knowledge distillation, which is formulated as follows...
Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework. AAAI 2020 Preparing Lessons: Improve Knowledge Distillation with Better Supervision. arXiv:1911.07471 Adaptive Regularization of Labels. arXiv:1908.05474
Knowledge Distillation Focusing on Old Class 现有的知识蒸馏方法不对特征图中属于不同类别的区域加以区分,旧模型会将新类别对应的区域认作背景,直接蒸馏会限制新模型的可塑性。为此,作者提出了FOD,只对visual feature中不属于新类别的区域进行蒸馏,即根据gt选取不包含新类别像素的visual tokens进行蒸馏。因为在生成mask...
Additionally, ‘knowledge distillation’, as discussed in [19], uses information from public datasets to improve the model’s ability to adapt. In contrast, ‘parameter regularization’, detailed in [20], limits the update process by imposing penalties on changes to important parameters, helping ...
Oil and Gas Distillation: Where Does EAM Fit in at Midstream?Midstream operators would benefit greatly from enhanced attention toward the incorporation of enterprise asset management solutions into their day-to-day processes.Read More Asset Management Does Our Dependence on Data Centers Disrupt EAM?Let...
相似性保留蒸馏(SPD,similarity-preserving distillation):具有先进特征蒸馏[36]技术的竞争对手;持续表示学习(CRL,Continual representation learning)[48]:我们首先复现了他们的方法,并在他们发布的基准上获得了所报告的结果。然后,我们将他们的方法应用于我们的领域增量式行人ReID基准,并在表3中报告这些新结果;Joint-CE...