Open-world machine learning: A review and new outlooks
Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …
existing studies are largely based on the closed-world assumption, which assumes that the …
Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning
Recent advancements in deep learning have brought significant improvements to plant
disease recognition. However, achieving satisfactory performance often requires high …
disease recognition. However, achieving satisfactory performance often requires high …
Lmc: Large model collaboration with cross-assessment for training-free open-set object recognition
Open-set object recognition aims to identify if an object is from a class that has been
encountered during training or not. To perform open-set object recognition accurately, a key …
encountered during training or not. To perform open-set object recognition accurately, a key …
Feature selection for classification with Spearman's rank correlation coefficient-based self-information in divergence-based fuzzy rough sets
J Jiang, X Zhang, Z Yuan - Expert Systems with Applications, 2024 - Elsevier
Feature selection facilitates uncertainty disposal and information mining, and it has received
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …
Towards trustworthy dataset distillation
Efficiency and trustworthiness are two eternal pursuits when applying deep learning in
practical scenarios. Considering efficiency, dataset distillation (DD) endeavors to reduce …
practical scenarios. Considering efficiency, dataset distillation (DD) endeavors to reduce …
Openmix+: Revisiting data augmentation for open set recognition
Open set recognition requires models to recognize samples of known classes learned in the
training set while reject unknowns not learned. Compared with the structural risk …
training set while reject unknowns not learned. Compared with the structural risk …
Unified classification and rejection: A one-versus-all framework
Classifying patterns of known classes and rejecting ambiguous and novel (also called as out-
of-distribution (OOD)) inputs are involved in open world pattern recognition. Deep neural …
of-distribution (OOD)) inputs are involved in open world pattern recognition. Deep neural …
Open set classification of gan-based image manipulations via a vit-based hybrid architecture
Classification of AI-manipulated content is receiving great attention, for distinguishing
different types of manipulations. Most of the methods developed so far fail in the open-set …
different types of manipulations. Most of the methods developed so far fail in the open-set …
Orientational clustering learning for open-set hyperspectral image classification
Recently, some literature has begun to pay attention to the open-set problem in remote
sensing application scenarios and studied various open-set hyperspectral image …
sensing application scenarios and studied various open-set hyperspectral image …
Improving open set recognition via visual prompts distilled from common-sense knowledge
Open Set Recognition (OSR) poses significant challenges in distinguishing known from
unknown classes. In OSR, the overconfidence problem has become a persistent obstacle …
unknown classes. In OSR, the overconfidence problem has become a persistent obstacle …