Multi-class Classification without Multi-class Labels (1901.00544v1)

Abstract: This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation. The proposed method, meta classification learning, optimizes a binary classifier for pairwise similarity prediction and through this process learns a multi-class classifier as a submodule. We formulate this approach, present a probabilistic graphical model for it, and derive a surprisingly simple loss function that can be used to learn neural network-based models. We then demonstrate that this same framework generalizes to the supervised, unsupervised cross-task, and semi-supervised settings. Our method is evaluated against state of the art in all three learning paradigms and shows a superior or comparable accuracy, providing evidence that learning multi-class classification without multi-class labels is a viable learning option.

• The paper introduces a novel meta classification framework that uses pairwise similarity to bypass traditional label requirements in multi-class classification.
• It demonstrates robust performance across supervised, unsupervised, and semi-supervised settings using a straightforward probabilistic graphical model.
• It reduces annotation costs and adapts to limited or noisy label scenarios by transferring learned similarities effectively.

Multi-class Classification without Multi-class Labels: Insights and Implications

The paper "Multi-class Classification without Multi-class Labels" introduces an innovative approach to classification, which circumvents the often prohibitive requirement of obtaining detailed, class-specific labels. Instead, the proposed methodology exploits pairwise similarity information amongst samples. This alternative form of supervision is not only weaker but also considerably more accessible and cost-effective.

Core Contributions and Methodology

The authors propose a meta classification learning framework where the task encapsulation is reversed. Contrary to traditional strategies, a multi-class classifier operates as a sub-module within a binary classifier. This binary classifier focuses on predicting pairwise similarities. The critical aspect of the formulation is an intuitive probabilistic graphical model that leads to a straightforward loss function for training neural networks.

The framework is evaluated across three learning paradigms:

1. Supervised Learning: The loss function and model are capable of leveraging binary pairwise similarity inputs derived from class labels.
2. Unsupervised Cross-task Transfer Learning: The model extends to scenarios with unknown classes by transferring learned similarities across different tasks, demonstrating superior performance over constrained clustering methods.
3. Semi-supervised Learning: The authors introduce a Pseudo-MCL strategy that integrates pseudo-similarity labeling with the existing labeled data, achieving performance on par with state-of-the-art semi-supervised learning techniques.

Experimental Evaluation and Results

The empirical results reinforce the efficacy of the proposed approach. Notably, the method achieves comparable classification accuracy to traditional cross-entropy methods in supervised settings, even when class labels are completely replaced by pairwise similarity information. In the unsupervised cross-task transfer learning setting, the framework outperforms several baselines, including sophisticated constrained clustering algorithms. This suggests a robust capacity for discovering latent class structures in unlabeled datasets. Further, in semi-supervised learning, Pseudo-MCL is shown to rival the performance of sophisticated approaches like Virtual Adversarial Training, providing a hyperparameter-free alternative that is attractive for low-data regimes.

Theoretical and Practical Implications

The work challenges traditional notions of label reliance in multi-class classification. By demonstrating that comparable performance can be achieved without class-specific labels, it highlights potential reductions in annotation costs and expands the applicability of classification methods to scenarios where class distinctions are ambiguous or expert labeling is impractical.

Furthermore, the unified framework accommodating multiple learning paradigms suggests a versatile tool that could adapt to varying levels of label availability and domain shifts. The method’s robustness to noisy constraints, as demonstrated in experiments with significant errors in similarity predictions, positions it as a viable option in real-world applications where label accuracy cannot be guaranteed.

Future Directions

This research opens several avenues for future exploration. An extension of the framework could incorporate adaptive strategies for noise mitigation in pairwise similarity data. Additionally, integrating domain adaptation and few-shot learning within the meta classification lens holds promise for future investigation. Another interesting direction would be optimizing non-tractable likelihood formulations that consider joint constraint optimization to potentially enhance performance in noisy environments.

In conclusion, this paper presents a methodologically sound alternative to standard label-intensive classification, expanding the horizon of how we conceptualize learning from labeled data.