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 ✨...
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。
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。
Despite its successes, deep learning has difficulty adapting to changing data. Because of this, in almost all applications, deep learning is restricted to a special training phase and then turned off when the network is actually used. For example, large language models such as ChatGPT are traine...
The agent has to traverse a dense area of static obstacles and reach the end of the arena. The base and height of each obstacle vary between 0.5 and 3 m. The environment is a rectangular flat arena (150 × 60 m2) with 50 random obstacles initialized at the start of each epis...
Dense Classification and Implanting for Few-Shot Learning [paper] Yann Lifchitz, Yannis Avrithis, Sylvaine Picard, Andrei Bursuc --CVPR 2019 Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu,...
From naturalistic driving data, the background agents learn what adversarial manoeuvre to execute through a dense deep-reinforcement-learning (D2RL) approach, in which Markov decision processes are edited by removing non-safety-critical states and reconnecting critical ones so that the information in ...
CNN is of 3 convolution layers, and then, layers incremented to six for same-session and cross-session databases. Activation function used is Relu and at the dense layer softmax. Loss is sparse categorical cross entropy loss. Training is 70%, and test data is 30%. The CNN architecture is...
It requires no modification to the original network, is simple to implement, and is applicable to a variety of deep models (sparse and dense, text and vision).” interpret "an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof.” ...
CoMoDA: Continuous Monocular Depth Adaptation Using Past Experiences WACV 2021 MonoRec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera CVPR 2021 [Daniel Cremmers] Plenoxels: Radiance Fields without Neural Networks Lidar with Velocity: Motion Distortion Correction of...