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Active generalized category discovery
Abstract Generalized Category Discovery (GCD) is a pragmatic and challenging open-world
task which endeavors to cluster unlabeled samples from both novel and old classes …
task which endeavors to cluster unlabeled samples from both novel and old classes …
[HTML][HTML] Managing the unknown in machine learning: Definitions, related areas, recent advances, and prospects
In the rapidly evolving domain of machine learning, the ability to adapt to unforeseen
circumstances and novel data types is of paramount importance. The deployment of Artificial …
circumstances and novel data types is of paramount importance. The deployment of Artificial …
Revisiting confidence estimation: Towards reliable failure prediction
Reliable confidence estimation is a challenging yet fundamental requirement in many risk-
sensitive applications. However, modern deep neural networks are often overconfident for …
sensitive applications. However, modern deep neural networks are often overconfident for …
Dual mean-teacher: An unbiased semi-supervised framework for audio-visual source localization
Abstract Audio-Visual Source Localization (AVSL) aims to locate sounding objects within
video frames given the paired audio clips. Existing methods predominantly rely on self …
video frames given the paired audio clips. Existing methods predominantly rely on self …
Rcl: Reliable continual learning for unified failure detection
Deep neural networks are known to be overconfident for what they don't know in the wild
which is undesirable for decision-making in high-stakes applications. Despite quantities of …
which is undesirable for decision-making in high-stakes applications. Despite quantities of …
Review on machine learning application in tissue engineering: What has been done so far? Application areas, challenges, and perspectives
Artificial intelligence and machine learning (ML) approaches have recently been getting
much of researchers' attention. The growing interest in these methods results from the fast …
much of researchers' attention. The growing interest in these methods results from the fast …
Pilora: Prototype guided incremental lora for federated class-incremental learning
Existing federated learning methods have effectively dealt with decentralized learning in
scenarios involving data privacy and non-IID data. However, in real-world situations, each …
scenarios involving data privacy and non-IID data. However, in real-world situations, each …
Towards extracting ethical concerns-related software requirements from app reviews
As mobile applications become increasingly integral to our daily lives, concerns about ethics
have grown drastically. Users share their experiences, report bugs, and request new …
have grown drastically. Users share their experiences, report bugs, and request new …
SelEx: Self-expertise in Fine-Grained Generalized Category Discovery
In this paper, we address Generalized Category Discovery, aiming to simultaneously
uncover novel categories and accurately classify known ones. Traditional methods, which …
uncover novel categories and accurately classify known ones. Traditional methods, which …
Enhancing Outlier Knowledge for Few-Shot Out-of-Distribution Detection with Extensible Local Prompts
Out-of-Distribution (OOD) detection, aiming to distinguish outliers from known categories,
has gained prominence in practical scenarios. Recently, the advent of vision-language …
has gained prominence in practical scenarios. Recently, the advent of vision-language …