Characterizing and Avoiding Negative Transfer (1811.09751v4)

Abstract: When labeled data is scarce for a specific target task, transfer learning often offers an effective solution by utilizing data from a related source task. However, when transferring knowledge from a less related source, it may inversely hurt the target performance, a phenomenon known as negative transfer. Despite its pervasiveness, negative transfer is usually described in an informal manner, lacking rigorous definition, careful analysis, or systematic treatment. This paper proposes a formal definition of negative transfer and analyzes three important aspects thereof. Stemming from this analysis, a novel technique is proposed to circumvent negative transfer by filtering out unrelated source data. Based on adversarial networks, the technique is highly generic and can be applied to a wide range of transfer learning algorithms. The proposed approach is evaluated on six state-of-the-art deep transfer methods via experiments on four benchmark datasets with varying levels of difficulty. Empirically, the proposed method consistently improves the performance of all baseline methods and largely avoids negative transfer, even when the source data is degenerate.

• The paper formally defines negative transfer and introduces an adversarial discriminator gate to filter out detrimental source data.
• It identifies key factors such as algorithm-specific effects, distributional divergence, and limited labeled target data as drivers of negative transfer.
• Empirical evaluations on benchmark datasets demonstrate consistent performance improvements across state-of-the-art deep transfer methods.

Characterizing and Avoiding Negative Transfer in Transfer Learning

In the paper "Characterizing and Avoiding Negative Transfer," the authors address a critical yet underexplored issue in transfer learning, termed negative transfer, where the transfer of knowledge from a source domain adversely impacts the performance on a target domain. Despite the recognized potential of transfer learning to address labeled data scarcity by leveraging data from related tasks, the risk of negative transfer remains a considerable barrier, especially when source and target domains are not closely aligned.

Core Contributions

The authors make several noteworthy contributions to the field by providing a formal definition of negative transfer and proposing a structured analysis that identifies its underlying factors. They propose a novel technique leveraging adversarial networks to mitigate negative transfer by filtering out detrimental source data. This technique is highly adaptable and can be integrated with various transfer learning algorithms. Their method is empirically validated on six state-of-the-art deep transfer methods applied to four benchmark datasets, showing consistent performance improvements and the reduction of negative transfer effects.

Formal Definition and Analysis

The paper begins with a formal definition of negative transfer as a condition that occurs when the expected risk of a transfer learning algorithm using both source and target data exceeds that when using only target data. This definition is significant as it allows for quantifiable measurement of negative transfer effects and shifts the perspective to a more algorithm-specific analysis. The authors then identify three primary factors contributing to negative transfer:

1. Algorithm-Specific Nature: Negative transfer is defined with respect to the transfer learning algorithm used. This requires the algorithm to be assessed on its performance with and without the inclusion of source data.
2. Distributional Divergence: The divergence between the joint distributions of source and target data (both marginal and conditional) is identified as fundamental to the occurrence of negative transfer. The authors emphasize that any successful transfer learning algorithm must correctly identify and exploit shared structures between these distributions.
3. Dependence on Labeled Target Data: Negative transfer becomes more pronounced when sufficient labeled target data is unavailable. The size of labeled target data inversely affects the ability to mitigate negative transfer, underscoring the necessity of balancing source and target information.

Proposed Method: Discriminator Gate

Envisioned as a solution to negative transfer, the authors introduce a discriminator gate method based on adversarial networks. This approach estimates density ratios through a discriminator that acts as a gating mechanism to filter unrelated source data while attempting to match both marginal and joint distributions. The use of adversarial training aims to achieve a better alignment between source and target domains in a way that emphasizes shared, beneficial information.

Empirical Evaluation

Experiments conducted across datasets with varying levels of domain shifts reveal that the proposed method effectively reduces negative transfer across different tasks. The empirical results demonstrate improvements over baseline methods, highlighting the efficacy of the proposed technique in alleviating the adverse effects of unrelated source data.

Implications and Future Directions

The research provides a comprehensive framework for understanding and addressing negative transfer in transfer learning. By deploying adversarial strategies to actively filter out less relevant source data, the proposed method sets the stage for more robust transfer learning applications across diverse domains.

For future exploration, it would be beneficial to extend these methods to more complex transfer learning scenarios, such as multi-source and multi-target domains, as well as exploring the robustness of these methods in dynamic and real-time learning environments.

This paper significantly contributes to our understanding of negative transfer and offers a viable pathway to harness the full potential of transfer learning while minimizing its risks. The demonstrated capability of reducing negative impacts aligns well with ongoing research efforts to develop adaptive systems capable of transferring knowledge more effectively across varying contexts and task settings.