Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Transfer Adaptation Learning: A Decade Survey (1903.04687v2)

Published 12 Mar 2019 in cs.CV

Abstract: The world we see is ever-changing and it always changes with people, things, and the environment. Domain is referred to as the state of the world at a certain moment. A research problem is characterized as transfer adaptation learning (TAL) when it needs knowledge correspondence between different moments/domains. Conventional machine learning aims to find a model with the minimum expected risk on test data by minimizing the regularized empirical risk on the training data, which, however, supposes that the training and test data share similar joint probability distribution. TAL aims to build models that can perform tasks of target domain by learning knowledge from a semantic related but distribution different source domain. It is an energetic research filed of increasing influence and importance, which is presenting a blowout publication trend. This paper surveys the advances of TAL methodologies in the past decade, and the technical challenges and essential problems of TAL have been observed and discussed with deep insights and new perspectives. Broader solutions of transfer adaptation learning being created by researchers are identified, i.e., instance re-weighting adaptation, feature adaptation, classifier adaptation, deep network adaptation and adversarial adaptation, which are beyond the early semi-supervised and unsupervised split. The survey helps researchers rapidly but comprehensively understand and identify the research foundation, research status, theoretical limitations, future challenges and under-studied issues (universality, interpretability, and credibility) to be broken in the field toward universal representation and safe applications in open-world scenarios.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (2)
  1. Lei Zhang (1689 papers)
  2. Xinbo Gao (194 papers)
Citations (162)
View on Semantic Scholar

Summary

Analysis of Transfer Adaptation Learning Methodologies

The paper "Transfer Adaptation Learning: A Decade Survey" presents a comprehensive survey of methodologies and challenges in transfer adaptation learning (TAL), an emerging field aiming to enable machine learning models to leverage knowledge from related domains. Authors Lei Zhang and Xinbo Gao offer an insightful exploration into the state of TAL over the last decade, categorizing it into five main technical challenges: instance re-weighting adaptation, feature adaptation, classifier adaptation, deep network adaptation, and adversarial adaptation.

Transfer adaptation learning seeks to address situations where training and test data are drawn from distinct domains—a scenario that contravenes the typical assumption of identical distributions in traditional machine learning paradigms. TAL's objective is to formulate models that are capable of recognizing samples in the target domain by utilizing knowledge from a semantically related source domain, notwithstanding the disparity in their data distributions.

Survey Classification

  1. Instance Re-weighting Adaptation: This approach attempts to reconcile distribution discrepancies by inferring resampling weights for instances from the source domain. Techniques like kernel mean matching (KMM), maximum mean discrepancy (MMD), and sample selection have been employed to estimate these weights, allowing TAL models to align source data closer to the target distribution.
  2. Feature Adaptation: Involves learning a robust feature representation that is invariant across different domains. Strategies like subspace alignment and discriminative projections help mitigate distribution discrepancies. Advanced methodologies incorporate deep learning models to achieve more nuanced domain invariant features through techniques such as zero-padding feature augmentation.
  3. Classifier Adaptation: Focuses on adapting classifiers from a source domain to be effective in a target domain. Methods like support vector machines (SVMs) and manifold regularization have been integrated into TAL frameworks to enhance recognition capabilities across differing domains. More recent efforts emphasize leveraging Bayesian frameworks for improved classifier generalization.
  4. Deep Network Adaptation: With deep neural networks becoming prevalent, TAL studies have shifted towards adapting network architectures to be transferable across domains. Techniques like marginal and conditional alignment are explored within deep learning frameworks to ensure effective feature transfer across domains.
  5. Adversarial Adaptation: Inspired by generative adversarial networks (GANs), adversarial learning engages in minimizing domain discrepancies by confounding domain classifiers, thereby promoting feature-level and pixel-level domain adaptation. This methodology has extended to scenarios requiring semantic adaptation.

Numerical Results and Theoretical Implications

The survey discusses critical advancements in TAL methodologies, highlighting the technical challenges impeding universal applicability. Theoretical contributions such as expected target error upper bounds provide context to the overarching goal of TAL: minimizing the marginal and conditional distribution gap between source and target domains. Future directions indicate a necessity for enhanced domain-invariant representation learning, potentially combining hybrid deep learning approaches.

Practical Implications and Speculation for AI Developments

Practically, TAL promises significant advancements in open-world AI applications, requiring robust algorithms capable of transferring knowledge across disparate domains without predefined supervised conditions. It envisions utilization across applications in vision tasks from object detection to semantic segmentation in scenarios where human labeling is infeasible. As such, TAL's trajectory is pivotal for safe and universal AI applications.

Conclusion

Overall, this survey serves as both a retrospection and anticipation of the TAL field, providing a roadmap for addressing its inherent challenges, with emphasis on the exploration of the universality, interpretability, and credibility of transfer learning models. While considerable progress has been made, future studies are expected to tackle the under-explored areas critically impacting TAL's practical deployment in real-world AI scenarios. The integration of transfer learning into domain-agnostic AI systems remains a promising yet challenging frontier.