[Paper Review] A Geometric Approach to Active Learning for Convolutional Neural Networks

Ozan Şener, Silvio Savarese|arXiv (Cornell University)|Aug 1, 2017

Machine Learning and Algorithms29 references27 citations

TL;DR

This paper proposes a geometric active learning method for CNNs that frames active learning as core-set selection, using data geometry to identify the most informative samples. By selecting a subset of images that best represent the data manifold, the method achieves significantly better performance than prior heuristics in image classification tasks.

ABSTRACT

Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labelled from a very large collection (ie. active learning). Our empirical study suggests that many of the active learning heuristics in the literature are not effective when applied to CNNs in batch setting. Inspired by these limitations, we define the problem of active learning as core-set selection, ie. choosing set of points such that a model learned over the selected subset is competitive for the remaining data points. We further present a theoretical result characterizing the performance of any selected subset using the geometry of the datapoints. As an active learning algorithm, we choose the subset which is expected to yield best result according to our characterization. Our experiments show that the proposed method significantly outperforms existing approaches in image classification experiments by a large margin.

Motivation & Objective

• To address the high cost of labeling large datasets in deep learning by improving active learning for CNNs.
• To overcome the limitations of existing active learning heuristics when applied to CNNs in batch settings.
• To frame active learning as core-set selection, where the goal is to select a representative subset that maintains model performance.

Proposed method

• The method formulates active learning as core-set selection, aiming to choose a subset of data points that yield competitive performance on the full dataset.
• It introduces a theoretical characterization of subset performance based on the geometric structure of data points.
• The algorithm selects the subset expected to yield the best performance according to the proposed geometric characterization.
• It leverages the intrinsic geometry of the data manifold to prioritize samples that maximize representativeness and diversity.

Experimental results

Research questions

• RQ1 Can geometric properties of data points be used to improve active learning performance in CNNs?
• RQ2 Why do standard active learning heuristics fail in batch training settings with CNNs?
• RQ3 How can core-set selection be formalized using data geometry to ensure high model performance?

Key findings

• The proposed geometric active learning method significantly outperforms existing approaches in image classification benchmarks.
• The method achieves better performance with fewer labeled examples compared to traditional active learning heuristics.
• Theoretical analysis shows that subset performance is closely tied to the geometric distribution of data points.
• Empirical results confirm that geometry-aware sampling leads to more representative and diverse training sets.