摘要: 在缺少明确的任务边界和任务标识的情况下,本文探索了task-free continual learning(任务具有独立的数据标签空间,在训练和测试的过程中不提供任务识别符), 在这个场景中需要一个可以避免灾难性遗忘的模型。在众多针对task-free CL的方法中,一个著名的方法家族是基于内存的方法,即存储和重放训练样本的子集。但是这...
Learning an evolved mixture model for task-free continual learning Recently, continual learning (CL) has gained significant interest because it enables deep learning models to acquire new knowledge without forgetting previ... F Ye,AG Bors - IEEE 被引量: 0发表: 2022年 Gradient-based Editing of ...
To avoid catastrophic forgetting, various continual learning (CL) approaches have been devised. However, these usually require discrete task boundaries. This requirement seems biologically implausible and often limits the application of CL methods in the real world where tasks are...
Methods proposed in the literature towards continual deep learning typically operate in a task-based sequential learning setup. A sequence of tasks is learned, one at a time, with all data of current task available but not of previous or future tasks. Task boundaries and identities are known at...
PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios. - continual-learning/main_task_free.py at master · gpubrr042/continual-learning
Online Task-Free Continual Learning (OTFCL) aims to learn novel concepts from streaming data without accessing task information. Most memory-based approaches used in OTFCL are not suitable for unsupervised learning because they require accessing supervised signals to implement their sample selection mecha...
Breadcrumbs continual-learning / compare_task_free.pyTop File metadata and controls Code Blame executable file· 293 lines (243 loc) · 10.2 KB Raw #!/usr/bin/env python3 import os import numpy as np from param_stamp import get_param_stamp_from_args from visual import visual_plt import ...
Multimodal multitask machine learning system for document intelligence tasks includes a feature extractor processing token values obtained from a document to obtain features, and a
Gradient Based Memory Editing for Task-Free Continual LearningXisen JinJunyi DuXiang Ren
Gradient-based Editing of Memory Examples for Online Task-free Continual LearningXisen JinArka SadhuJunyi DuXiang RenNeural Information Processing Systems