Continually trained life-long classification
Sign Up to like & getrecommendations! Published in 2022 at "Neural Computing and Applications"
DOI: 10.1007/s00521-021-06154-9
Abstract: Two challenges can be found in a life-long classifier that learns continually: the concept drift, when the probability distribution of data is changing in time, and catastrophic forgetting when the earlier learned knowledge is lost.… read more here.
Keywords: life; concept drift; catastrophic forgetting; long classification ... See more keywords
Continual Pedestrian Trajectory Learning With Social Generative Replay
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3231833
Abstract: Learning to predict the trajectories of pedestrians is essential for improving safety and efficiency of mobile robots. The prediction is challenging since the robot needs to operate in multiple environments in which the motion patterns… read more here.
Keywords: catastrophic forgetting; generative replay; pedestrian trajectory; prediction ... See more keywords
Curiosity-Driven Class-Incremental Learning via Adaptive Sample Selection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2022.3196092
Abstract: Modern artificial intelligence systems require class-incremental learning while suffering from catastrophic forgetting in many real-world applications. Due to the missing knowledge of past data, performance substantially degrades. Recent methods often used knowledge distillation and bias… read more here.
Keywords: sample; curiosity driven; incremental learning; catastrophic forgetting ... See more keywords
Incremental Embedding Learning With Disentangled Representation Translation.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3199816
Abstract: Humans are capable of accumulating knowledge by sequentially learning different tasks, while neural networks fail to achieve this due to catastrophic forgetting problems. Most current incremental learning methods focus more on tackling catastrophic forgetting for… read more here.
Keywords: disentangled representation; representation translation; catastrophic forgetting; embedding networks ... See more keywords
Handling catastrophic forgetting using cross-domain order in incremental deep learning
Sign Up to like & getrecommendations! Published in 2023 at "Journal of Electronic Imaging"
DOI: 10.1117/1.jei.32.2.023036
Abstract: Abstract. In the present era of big data applications, incremental learning has emerged as the most admired area of research where ever-ending tasks from different application domains arrive temporarily, and the learning models majorly focus… read more here.
Keywords: catastrophic forgetting; cross; deep learning; cross domain ... See more keywords
Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning
Sign Up to like & getrecommendations! Published in 2019 at "Neural Computation"
DOI: 10.1162/neco_a_01232
Abstract: Humans are able to master a variety of knowledge and skills with ongoing learning. By contrast, dramatic performance degradation is observed when new tasks are added to an existing neural network model. This phenomenon, termed… read more here.
Keywords: adversarial feature; catastrophic forgetting; feature alignment; new tasks ... See more keywords