Active Decision Boundary Annotation with Deep Generative Models
This paper is on active learning where the goal is to reduce the data annotation burden by interacting with a (human) oracle during training. Standard active learning methods ask the oracle to annotate data samples. Instead, we take a profoundly different approach: we ask for annotations of the decision boundary. We achieve this using a deep generative model to create novel instances along a 1d line. A point on the decision boundary is revealed where the instances change class. Experimentally we show on three data sets that our method can be plugged-in to other active learning schemes, that human oracles can effectively annotate points on the decision boundary, that our method is robust to annotation noise, and that decision boundary annotations improve over annotating data samples.
Code (1)
Tasks
Active LearningSimilar Papers 제목 키워드 기반
Adversarial Active Learning for Deep Networks: a Margin Based Approach
We propose a new active learning strategy designed for deep neural networks. The goal is to minimize the number of data annotation queried from an oracle during training. Previous active learning strategies scalable for …
Active LearningPromptAL: Sample-Aware Dynamic Soft Prompts for Few-Shot Active Learning
Active learning (AL) aims to optimize model training and reduce annotation costs by selecting the most informative samples for labeling. Typically, AL methods rely on the empirical distribution of labeled data to define …
Active LearningActive Learning: Sampling in the Least Probable Disagreement Region
Active learning strategy to query samples closest to the decision boundary can be an effective strategy for sampling the most uncertain and thus informative samples. This strategy is valid only when the sample's "closene…
Active LearningvalidLeast Probable Disagreement Region for Active Learning
Active learning strategy to query unlabeled samples nearer the estimated decision boundary at each step has been known to be effective when the distance from the sample data to the decision boundary can be explicitly eva…
Active LearningBoundary-Centric Clip-Budgeted Active Learning for Temporal Action Segmentation
Temporal action segmentation (TAS) in untrimmed videos requires dense temporal supervision. However, most of the annotation cost is spent identifying action transitions where segmentation errors concentrate and small tem…
Action SegmentationActive Learning