An appraisal of incremental learning methods
As a special case of machine learning, incremental learning can acquire useful knowledge
from incoming data continuously while it does not need to access the original data. It is …
from incoming data continuously while it does not need to access the original data. It is …
Generative models from the perspective of continual learning
Which generative model is the most suitable for Continual Learning? This paper aims at
evaluating and comparing generative models on disjoint sequential image generation tasks …
evaluating and comparing generative models on disjoint sequential image generation tasks …
Learn#: A Novel incremental learning method for text classification
G Shan, S Xu, L Yang, S Jia, Y **ang - Expert Systems with Applications, 2020 - Elsevier
Deep learning is an effective method for extracting the underlying information in text.
However, it performs better on closed datasets and is less effective in real-world scenarios …
However, it performs better on closed datasets and is less effective in real-world scenarios …
A new knowledge distillation for incremental object detection
L Chen, C Yu, L Chen - 2019 International Joint Conference on …, 2019 - ieeexplore.ieee.org
Nowadays, the Convolutional Neural Network is successfully applied to the images object
detection. When new classes of object emerges, it is popular to adapt the convolutional …
detection. When new classes of object emerges, it is popular to adapt the convolutional …
Measuring asymmetric gradient discrepancy in parallel continual learning
Abstract In Parallel Continual Learning (PCL), the parallel multiple tasks start and end
training unpredictably, thus suffering from training conflict and catastrophic forgetting issues …
training unpredictably, thus suffering from training conflict and catastrophic forgetting issues …
Marginal replay vs conditional replay for continual learning
We present a new replay-based method of continual classification learning that we term
“conditional replay” which generates samples and labels together by sampling from a …
“conditional replay” which generates samples and labels together by sampling from a …
Elastic Multi-Gradient Descent for Parallel Continual Learning
The goal of Continual Learning (CL) is to continuously learn from new data streams and
accomplish the corresponding tasks. Previously studied CL assumes that data are given in …
accomplish the corresponding tasks. Previously studied CL assumes that data are given in …
Continual learning: Tackling catastrophic forgetting in deep neural networks with replay processes
T Lesort - arxiv preprint arxiv:2007.00487, 2020 - arxiv.org
Humans learn all their life long. They accumulate knowledge from a sequence of learning
experiences and remember the essential concepts without forgetting what they have learned …
experiences and remember the essential concepts without forgetting what they have learned …
Towards stable training of parallel continual learning
Parallel Continual Learning (PCL) tasks investigate the training methods for continual
learning with multi-source input, where data from different tasks are learned as they arrive …
learning with multi-source input, where data from different tasks are learned as they arrive …
Apprentissage continu: S'attaquer à l'oubli foudroyant des réseaux de neurones profonds grâce aux méthodes à rejeu de données
T Lesort - 2020 - theses.hal.science
Humans learn all their life long. They accumulate knowledge from a sequence of learning
experiences and remember the essential concepts without forgetting what they have learned …
experiences and remember the essential concepts without forgetting what they have learned …