Learning with Biased Complementary Labels (1711.09535v3)

Abstract: In this paper, we study the classification problem in which we have access to easily obtainable surrogate for true labels, namely complementary labels, which specify classes that observations do \textbf{not} belong to. Let $Y$ and $\bar{Y}$ be the true and complementary labels, respectively. We first model the annotation of complementary labels via transition probabilities $P(\bar{Y}=i|Y=j), i\neq j\in{1,\cdots,c}$, where $c$ is the number of classes. Previous methods implicitly assume that $P(\bar{Y}=i|Y=j), \forall i\neq j$, are identical, which is not true in practice because humans are biased toward their own experience. For example, as shown in Figure 1, if an annotator is more familiar with monkeys than prairie dogs when providing complementary labels for meerkats, she is more likely to employ "monkey" as a complementary label. We therefore reason that the transition probabilities will be different. In this paper, we propose a framework that contributes three main innovations to learning with \textbf{biased} complementary labels: (1) It estimates transition probabilities with no bias. (2) It provides a general method to modify traditional loss functions and extends standard deep neural network classifiers to learn with biased complementary labels. (3) It theoretically ensures that the classifier learned with complementary labels converges to the optimal one learned with true labels. Comprehensive experiments on several benchmark datasets validate the superiority of our method to current state-of-the-art methods.

• The paper proposes a novel framework for learning with biased complementary labels by estimating transition probabilities and modifying loss functions to align with true labels.
• Empirical results demonstrate significant accuracy improvements, exceeding 10% gains on benchmark datasets like MNIST and CIFAR, showing robustness even with non-ideal conditions.
• This research has practical implications for weakly supervised learning scenarios where true labels are scarce, enabling more resource-efficient AI systems in various applications.

Learning with Biased Complementary Labels: A Comprehensive Analysis

The paper "Learning with Biased Complementary Labels" proposes a novel framework for handling classification tasks utilizing complementary labels—labels which specify classes that observations do not belong to. This work addresses critical aspects in weakly supervised learning settings where true labels are expensive or difficult to obtain. Here, the authors propose handling these situations by leveraging complementary labels which are more readily acquired. Importantly, they extend beyond previous assumptions of uniform complementary label selection, addressing realistic scenarios where labeling biases exist.

Contribution Breakdown

The paper introduces three main innovations in learning with biased complementary labels:

1. Estimation of Transition Probabilities: The framework estimates transition probabilities without bias. The transition matrix $P(\bar{Y} = i | Y = j), i \neq j$, which models the annotation process, is crucial in understanding how complementary labels are assigned in different contexts. Here, the authors move away from uniform probability assumptions, acknowledging that human biases influence label annotation.
2. Modification of Loss Functions: The research provides a method to modify traditional loss functions such as the cross-entropy loss to be compatible with complementary labels. The modifications enable existing classifiers, particularly deep neural networks, to adapt accordingly and ensure consistency with optimal classifiers trained on true labels.
3. Theoretical Guarantees of Convergence: The paper critically includes proofs that classifiers trained under this framework theoretically converge to those trained with true labels, given sufficient training data. This assertion is supported by comprehensive empirical validations demonstrating the method's superiority over state-of-the-art approaches.

Numerical and Empirical Highlights

The authors support their theoretical claims with extensive experimentation across various datasets such as MNIST, CIFAR-10, CIFAR-100, and Tiny ImageNet. The results indicate significant accuracy improvements over previous methods, with the framework achieving accuracy gains exceeding 10% in benchmark datasets. Particularly noteworthy is the robustness observed with the non-invertible transition matrices, indicating practical applicability beyond ideal conditions.

Implications and Future Directions

Practically, this research has implications for situations with limited access to true labels but where complementary label information can be cost-effectively collected. This has potential applications in fields where label scarcity is a problem, such as rare disease diagnostics or niche image classification tasks.

Theoretically, the paper opens up opportunities to explore understanding biases in human labeling processes and how these can be leveraged or corrected in machine learning frameworks. The future discourse could also explore the development of automated systems that optimize complementary label selection or distribution, enhancing training efficacy.

Speculative Outlook on AI Developments

The methods proposed offer a glimpse into more resource-efficient AI systems that can capitalize on alternative label approaches, broadening the horizons for machine learning applicability. With the evolution of explainable AI, understanding biases in labeling and their potential corrections will be valuable.

In conclusion, this paper lays a robust foundation for further exploration into weak supervision methodologies, emphasizing adaptability and efficiency in label acquisition. The pragmatic and theoretical advancements presented contribute significantly to contemporary AI research and application.