Positive Transfer Paradigm

A Positive Transfer Paradigm is a automated learning paradigm that improves performance on a target task by leveraging knowledge acquired from a related source task or domain.

AKA: Helpful Transfer, Transfer Enhancement, Constructive Transfer.
Context:
- It can occur when source and target tasks share semantic, syntactic, or structural features.
- It can improve sample efficiency in downstream fine-tuning during transfer learning.
- It can support generalization in AI text generation when pretraining and target data domains are aligned (e.g., both encyclopedic or formal writing).
- It can benefit multi-task models when related tasks reinforce shared representations (e.g., summarization and translation).
- It can be enhanced through techniques like curriculum learning or domain-aware pretraining.
- It can be measured through gains in target-task performance (e.g., increased BLEU, ROUGE, or human preference scores).
- It can occur in both supervised and self-supervised learning paradigms.
- ...
Example(s):
- Pretrained Language Models improving downstream performance on domain-specific tasks like biomedical text generation.
- Multi-Task Learning setups where POS tagging improves named entity recognition.
- Domain Adaptive Pretraining (DAPT) where continuing pretraining on target-domain corpora boosts final generation quality.
- ...
See: Transfer Learning, Multi-Task Learning, Domain Adaptation, AI Text Generation Task, Curriculum Learning, Negative Transfer.

References

(Lenton et al., 2021) ⇒ Daniel Lenton, Fabio Pardo, Fabian Falck, Stephen James, & Ronald Clark. (2021). "Ivy: Templated Deep Learning for Inter-Framework Portability". In: arXiv Preprint.
- QUOTE: Through our evaluations, we show that Ivy can significantly reduce lines of code with a runtime overhead of less than 1% in most cases. ... Ivy enables positive transfer of deep learning models and functionality across frameworks, facilitating reuse and adaptation of code and models without reimplementation. This demonstrates how a unified abstraction can enhance positive transfer in practical machine learning workflows.

(Mathur et al., 2020) ⇒ Nitika Mathur, Timothy Baldwin, & Trevor Cohn. (2020). "Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics". In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
- QUOTE: Our analysis reveals that improvements in automatic metrics can lead to positive transfer in machine translation evaluation, where better metrics facilitate more accurate system rankings and closer alignment with human judgments. This demonstrates how advances in one evaluation domain can enhance performance and reliability in another, exemplifying positive transfer in evaluation protocol design.

(Qi & Luo, 2019) ⇒ Guo-Jun Qi & Jiebo Luo. (2019). "Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods". In: arXiv Preprint.
- QUOTE: Many unsupervised learning and semi-supervised learning methods leverage positive transfer by utilizing unlabeled data to improve model generalization on small labeled datasets. Techniques such as domain adaptation and self-supervised representation learning are designed to maximize positive transfer from related tasks or domains, enhancing learning efficiency and performance in data-scarce scenarios.

(Pan & Yang, 2010) ⇒ Sinno Jialin Pan & Qiang Yang. (2010). "A Survey on Transfer Learning". In: Journal of Machine Learning Research.
- QUOTE: Positive transfer occurs when knowledge or skills from a source task improve performance on a target task, which is the central goal of transfer learning. The survey outlines conditions that favor positive transfer, such as high similarity between source and target domains, and reviews methods that promote positive transfer while minimizing the risk of negative transfer.

(Shen et al., 2020) ⇒ Yilun Shen, Zhiwei Steven Wu, Weijie J. Su, & Zhiwei Steven Wu. (2020). "Positive-Unlabeled Learning with Non-Negative Risk Estimator". In: arXiv Preprint.
- QUOTE: We propose a non-negative risk estimator for positive-unlabeled learning that guarantees positive transfer from labeled positive data to unlabeled data. Our approach ensures that learning from positive data improves model performance on the broader unlabeled dataset, demonstrating the effectiveness of positive transfer in semi-supervised settings.