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Found 4 papers, 3 papers with code
Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation
These limitations include a lack of principled ways to determine optimal hyperparameters and performance degradation when the unlabeled target data fail to meet certain requirements such as a closed-set and identical label distribution to the source data.
Source-Free Domain Adaptation
Unsupervised Domain Adaptation
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions
Active learning aims to identify the most informative data from an unlabeled data pool that enables a model to reach the desired accuracy rapidly.
A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks
Therefore, we introduce the unknown detection task, an integration of previous individual tasks, for a rigorous examination of the detection capability of deep neural networks on a wide spectrum of unknown samples.
Confidence-Aware Learning for Deep Neural Networks
Despite the power of deep neural networks for a wide range of tasks, an overconfident prediction issue has limited their practical use in many safety-critical applications.
