Active Learning
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Benchmarks
CIFAR10 (10,000)
Most implemented
Active Learning for Convolutional Neural Networks: A Core-Set Approach
Self-Regulated Interactive Sequence-to-Sequence Learning
Learning Loss for Active Learning
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Variational Adversarial Active Learning
Few-Shot Learning with Graph Neural Networks
Papers
One Loop, Two Gains: Can Active Learning win the Lottery for Free?
The lottery ticket hypothesis posits the existence of winning tickets: sparse subnetworks that, when trained in isolation from their original initialization, match the accuracy of the full dense network. The predominant …
Image ClassificationActive LearningTrain Smarter, Not Harder: Switching Signal-Guided Training in Active Learning
Training strategy, namely whether to retrain from scratch or fine-tune from the previous checkpoint, is an overlooked decision variable in active learning. We show that this choice has exploitable structure: retraining i…
Active LearningCost-efficient Active Learning for Referring Image Segmentation and Grounding
Collecting natural-language referring expressions along with region annotations, such as masks or boxes, is a major bottleneck in visual grounding (VG), as annotators must write descriptions that distinguish target regio…
Image SegmentationVisual GroundingActive LearningLoss-Based Active Learning for Neural Abstractive Summarization
Fine-tuning abstractive summarization models requires high-quality annotated data. However, obtaining such corpora is expensive and time-consuming, as it requires human annotators to read and comprehend long documents to…
Active LearningConfAL-WM: Confidence-Guided Active Learning for Action-Conditioned World Models
Action-conditioned world models have become an important foundation for embodied prediction, planning, and synthetic data generation, but their errors under new task and scene distributions are often concentrated in loca…
Synthetic Data GenerationActive LearningDiversity-Based Active Learning: An Evaluation of Metric Spaces for Active Learning Selection
With rapid advancement over the last few years, many different methods are now widely used for classification. However, training these models requires substantial labeled data. Active Learning is a potential solution to …
Active Learning