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Found 5 papers, 1 papers with code
Active and Incremental Learning with Weak Supervision
By selecting unlabeled examples that are promising in terms of model improvement and only asking for respective labels, active learning can increase the efficiency of the labeling process in terms of time and cost.
Active Learning for Deep Object Detection
In this paper, we combine a novel method of active learning for object detection with an incremental learning scheme to enable continuous exploration of new unlabeled datasets.
Information-Theoretic Active Learning for Content-Based Image Retrieval
We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval.
Fast Learning and Prediction for Object Detection using Whitened CNN Features
We combine features extracted from pre-trained convolutional neural networks (CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of both: the high detection performance of CNNs, automatic feature engineering, fast model learning from few training samples and efficient sliding-window detection.
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes
The demands on visual recognition systems do not end with the complexity offered by current large-scale image datasets, such as ImageNet.
