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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 …
[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 …
Entropic open-set active learning
Active Learning (AL) aims to enhance the performance of deep models by selecting the most
informative samples for annotation from a pool of unlabeled data. Despite impressive …
informative samples for annotation from a pool of unlabeled data. Despite impressive …
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 …
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 …
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 …
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 …
Overcoming common flaws in the evaluation of selective classification systems
Selective Classification, wherein models can reject low-confidence predictions, promises
reliable translation of machine-learning based classification systems to real-world scenarios …
reliable translation of machine-learning based classification systems to real-world scenarios …
Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection
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 …