Learning a Deep ConvNet for Multi-label Classification with Partial Labels (1902.09720v1)

Abstract: Deep ConvNets have shown great performance for single-label image classification (e.g. ImageNet), but it is necessary to move beyond the single-label classification task because pictures of everyday life are inherently multi-label. Multi-label classification is a more difficult task than single-label classification because both the input images and output label spaces are more complex. Furthermore, collecting clean multi-label annotations is more difficult to scale-up than single-label annotations. To reduce the annotation cost, we propose to train a model with partial labels i.e. only some labels are known per image. We first empirically compare different labeling strategies to show the potential for using partial labels on multi-label datasets. Then to learn with partial labels, we introduce a new classification loss that exploits the proportion of known labels per example. Our approach allows the use of the same training settings as when learning with all the annotations. We further explore several curriculum learning based strategies to predict missing labels. Experiments are performed on three large-scale multi-label datasets: MS COCO, NUS-WIDE and Open Images.

Overview of "Learning a Deep ConvNet for Multi-label Classification with Partial Labels"

The paper "Learning a Deep ConvNet for Multi-label Classification with Partial Labels" by Thibaut Durand, Nazanin Mehrasa, and Greg Mori presents a significant advancement in the domain of multi-label image classification. The authors address a pivotal challenge of labeling and learning in multi-label settings, where acquiring exhaustive annotations can be prohibitive. Their proposal to use partial labels—where only some labels per image are known—aims to reduce the annotation burden while maintaining effective model performance.

Contributions

1. Labeling Strategies for Multi-label Datasets: The authors perform an empirical evaluation of different labeling strategies to identify the most effective method for multi-label datasets with a constrained labeling budget. Their findings suggest that partially labeling all images in a dataset is more efficient than fully labeling a smaller subset of images.
2. Partial-BCE Loss Function: The cornerstone of this work is the introduction of a novel classification loss function tailored for partial labels, known as the partial-Binary Cross-Entropy (partial-BCE) loss. This loss function adapts to the proportion of known labels per example, offering improved learning dynamics over the standard BCE loss when data is partially annotated.
3. Label Prediction Using Curriculum Learning: The authors propose curriculum learning strategies to predict missing labels. Leveraging the gradual complexification principle from curriculum learning, they integrate Bayesian uncertainty to iteratively predict and refine missing labels, which improves overall model robustness.
4. Graph Neural Network for Label Correlations: To effectively model label dependencies, a Graph Neural Network (GNN) is employed on top of the ConvNet structure. This addition captures the correlations among labels, enhancing the prediction accuracy of the multi-label classifier.

Key Findings

The experiments conducted with the MS COCO, NUS-WIDE, and Open Images datasets demonstrate the efficacy of the proposed partial-BCE and curriculum learning strategies. Remarkably, the paper provides quantitative evidence showing that the partial-BCE loss function significantly outperforms traditional BCE, with improvements particularly pronounced when the proportion of known labels is small. Additionally, the use of GNNs is shown to consistently boost performance across different datasets.

Another noteworthy observation is that under the partial labeling strategy, collecting clean partial labels results in better generalization than using fully labeled but noisy datasets. This insight underscores the detrimental impact of label noise and the potential for partial annotations to serve as an efficient alternative in resource-constrained settings.

Implications and Future Directions

This paper opens several avenues for future exploration within the AI community. The proposed framework's adaptability to large-scale datasets highlights its applicability across various domains where obtaining exhaustive labels is a challenge. Additionally, the curriculum learning mechanism can inspire further research into dynamic strategies for label refinement that incorporate advanced uncertainty quantification methods, potentially beyond Bayesian approaches.

In theoretical terms, this work prompts deeper investigation into loss functions that accommodate varying label proportions, potentially sparking novel mathematical formulations that extend beyond the partial-BCE. Practically, the deployment of such models in real-world scenarios—like autonomous driving or medical diagnosis, where certain labels might invariably be missing—holds considerable promise.

In conclusion, this paper contributes effectively to the growing body of work on multi-label classification by introducing scalable, robust methodologies to leverage partial annotations. The intersection of partial-BCE loss with graph-based learning strategies marks a meaningful advancement, pushing the boundaries of how neural networks can learn amidst incomplete data structures.