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
Scientific Discovery under Validation Congestion via Multi-Fidelity Pairwise Rankings
Modern computational methods can now propose candidate molecules, materials, and other scientific designs at an unprecedented scale, creating a validation congestion where candidates are abundant, but experimental capaci…
Active LearningDrug DiscoveryCalibration as a First-Class Criterion in LLM Evaluation
Calibration of language models -- the alignment between expressed or implicit confidence and empirical correctness -- is a well-studied subfield within NLP. Methods to measure it already exist. The problem is adoption: o…
Synthetic Data GenerationActive LearningOne 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 Learning