Results 71 to 80 of about 842,651 (374)
Class Incremental Learning With Large Domain Shift
IEEE AccessWe address an important and practical problem facing deep-learning-based image classification: class incremental learning with a large domain shift. Most previous efforts on class incremental learning focus on one aspect of the problem, i.e., learning to
Kamin Lee +4 more
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose Although not always achieved, complete chemotherapy‐induced nausea and vomiting (CINV) control is the conventional goal of CINV prophylaxis. In this two‐center, mixed‐methods study, we sought to understand the preferences of adolescent patients and family caregivers for CINV control endpoints.
Haley Newman +8 more
wiley +1 more source
Class Rectification Hard Mining for Imbalanced Deep Learning
, 2017 Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and extremely imbalanced among different attribute classes.
Dong, Qi, Gong, Shaogang, Zhu, Xiatian
core +1 more source
Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
Effective Decision Boundary Learning for Class Incremental Learning
CoRR, 2023 Rehearsal approaches in class incremental learning (CIL) suffer from decision boundary overfitting to new classes, which is mainly caused by two factors: insufficiency of old classes data for knowledge distillation and imbalanced data learning between the learned and new classes because of the limited storage memory.
Kunchi Li, Jun Wan 0001, Shan Yu
openaire +2 more sources
RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Mitochondrial diseases are the most common inherited metabolic disorders, characterized by pronounced clinical and genetic heterogeneity that complicates molecular diagnosis. Although DNA‐based sequencing approaches have become standard in genetic testing, up to half of patients remain without a definitive diagnosis.
Zhimei Liu +21 more
wiley +1 more source
Hyperspectral Image Classification Based on Class-Incremental Learning with Knowledge Distillation
Remote Sensing, 2022 By virtue of its large-covered spatial information and high-resolution spectral information, hyperspectral images make lots of mapping-based fine-grained remote sensing applications possible.
Meng Xu +3 more
doaj +1 more source
OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning [PDF]
Computer Vision and Pattern RecognitionFew-Shot Class-Incremental Learning (FSCIL) intro-duces a paradigm in which the problem space expands with limited data. FSCIL methods inherently face the chal-lenge of catastrophic forgetting as data arrives incremen-tally, making models susceptible to ...
Noor Ahmed, A. Kukleva, B. Schiele
semanticscholar +1 more source
Class-Incremental Learning via Knowledge Amalgamation
, 2023 Paper accepted at ECML PKDD ...
Marcus de Carvalho +3 more
openaire +3 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio +8 more
wiley +1 more source