Learning Dense and Continuous Optical Flow from an Event Camera IEEE Transactions on Image Processing (TIP 2022) Zhexiong Wan, Yuchao Dai, Yuxin Mao Project Page, arXiv, IEEE, Supp. If you have any questions, please do not hesitate to raise the issue or contact my email wanzhexiong@mail....
四、overcoming catastrophic forgetting in neural networks 这里引入一个新东西,连续学习(【增量学习】continuous learning),又叫序列学习,即学习一个有顺序的任务。人脑的神经元数量是有限的,故而在人脑的整理学习过程中,不会出现应对一个新的问题就重新规划问题,而是对已有的神经元组合进行修改,使之能够适应于持续学习。
DeepSDF(Deep Learning Continuous Signed Distance Functions for Shape Representation)是一种用于3D形状表示的深度学习方法。这项工作的核心在于使用连续有符号距离函数(Signed Distance Functions, SDFs)来表示3D物体的形状。SDFs是一种体积场,其值表示空间中每个点到最近物体表面的距离,正负号表示点位于表面内部还是外部。
动作(Action):动作指智能体和环境产生交互的所有行为的集合。不同的环境允许不同种类的动作,在给定的环境中,有效动作的集合经常被称为动作空间(action space),包括离散动作空间(discrete action spaces)和连续动作空间(continuous action spaces),例如,走迷宫机器人如果只有东南西北这 4 种移动方式,则其为离散动作空间...
Incremental Learning Repository: A collection of documents, papers, source code, and talks for incremental learning. Keywords: Incremental Learning, Continual Learning, Continuous Learning, Lifelong Learning, Catastrophic Forgetting CATALOGUE Quick Start ✨ Survey ✨ Papers by Categories ✨ Datasets ✨...
Besides, due to the dense characteristics in the raw EEG data, analysis of the streaming data is computationally more expensive, which poses a challenge for the model architecture selection. A proper model should be designed relatively with less training parameters. This is one reason why the ...
resenting3Dgeometryforrenderingandreconstruction. Theseprovidetrade-offsacrossfidelity,efficiencyandcom- pressioncapabilities.Inthiswork,weintroduceDeepSDF, alearnedcontinuousSignedDistanceFunction(SDF)rep- resentationofaclassofshapesthatenableshighqual- ...
Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders A workflow that segments anatomical structures in slit-lamp images and that annotates pathological features in each image improves the performance of a deep-learning algorith...
The pooling layer is for reducing the input size and avoiding overfitting. The input for the last dense layer is the flattened features from the convolutional and pooling layers, and the forecasting is made in this dense layer. 1D CNN can be deployed for simple applications, while more ...
For overcoming catastrophic forgetting, learning systems must, on the one hand, show the ability to acquire new knowledge and refine existing knowledge on the basis of the continuous input and, on the other hand, prevent the novel input from significantly interfering with existing knowledge. The ex...