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Learning to Transfer Examples for Partial Domain Adaptation

TL;DR

Proposed Example Transfer Network (ETN) to mitigate negative transfer in Partial Domain Adaptation, achieving SOTA on benchmarks like Office-31.

cs.CV 🔴 Advanced 2019-03-29 39 views
Zhangjie Cao Kaichao You Mingsheng Long Jianmin Wang Qiang Yang
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partial domain adaptation transfer learning deep learning negative transfer domain-invariant features

Key Findings

Methodology

ETN integrates domain-invariant feature learning with a progressive weighting scheme to quantify source example transferability. It uses auxiliary domain discriminators and label predictors to enhance weight discrimination, improving both source classifier and domain alignment.

Key Results

  • On Office-31 A→W task, ETN achieved 94.5% accuracy, outperforming PADA's 86.5% by 8%.
  • On Office-Home, ETN achieved an average accuracy of 71.9% across 12 tasks, significantly higher than IWAN's 61.3%.
  • On ImageNet→Caltech, ETN reached 75.1% accuracy, surpassing all baselines.

Significance

ETN addresses negative transfer in PDA by effectively filtering irrelevant source examples and enhancing positive transfer. Its innovative weighting mechanism and auxiliary modules provide a robust framework for transferring knowledge from large labeled datasets to smaller unlabeled domains, with significant implications for both research and practical applications.

Technical Contribution

ETN introduces a novel progressive weighting mechanism and auxiliary modules that combine label and domain information to quantify transferability. It simultaneously optimizes source classifiers and domain discriminators, ensuring positive transfer while mitigating negative transfer.

Novelty

ETN is the first to integrate label information into auxiliary domain discriminators for PDA, significantly improving weight discrimination. It outperforms existing methods by addressing both domain alignment and source classifier interference from irrelevant classes.

Limitations

  • ETN has high computational complexity due to additional auxiliary modules.
  • The weighting mechanism may underperform with extremely limited target domain samples.
  • The model is sensitive to hyperparameter tuning, requiring careful optimization.

Future Work

Future work could explore ETN's application to open-set domain adaptation, improve computational efficiency, and enhance robustness in scenarios with very limited target domain data.

AI Executive Summary

Partial Domain Adaptation (PDA) enables knowledge transfer from large labeled datasets to smaller unlabeled ones, where the target domain's label space is a subset of the source domain. However, existing methods often suffer from negative transfer due to irrelevant source classes.

The proposed Example Transfer Network (ETN) addresses this challenge by introducing a progressive weighting mechanism to quantify source example transferability. ETN incorporates auxiliary domain discriminators and label predictors to enhance the precision of these weights, ensuring that only relevant examples contribute to the target task. This approach simultaneously optimizes the source classifier and domain alignment, reducing negative transfer.

Experiments on benchmarks like Office-31, Office-Home, and ImageNet-Caltech demonstrate ETN's state-of-the-art performance. For instance, it achieved 94.5% accuracy on the A→W task of Office-31, outperforming PADA by 8%. While ETN's computational complexity and sensitivity to hyperparameters pose challenges, its innovative approach offers a promising direction for future research and applications in transfer learning.

Deep Analysis

Background

Domain adaptation aims to address distribution shifts between source and target domains. Traditional methods assume identical label spaces, but real-world scenarios often involve partial domain adaptation (PDA), where the target label space is a subset of the source. Existing methods like DANN and PADA struggle with negative transfer caused by irrelevant source classes.

Core Problem

The core challenge in PDA is transferring relevant examples from the source domain while ignoring irrelevant ones, especially when the target label space is unknown during training. This is difficult due to domain discrepancies and the lack of labeled target data.

Innovation

Key innovations of ETN include:

  • �� A progressive weighting mechanism to quantify source example transferability.
  • �� Auxiliary domain discriminators and label predictors that integrate label and domain information to enhance weight discrimination.
  • �� Simultaneous optimization of source classifiers and domain discriminators to ensure positive transfer and mitigate negative transfer.

Methodology

ETN's methodology involves:

  • �� Learning domain-invariant features using adversarial training.
  • �� Quantifying source example transferability via auxiliary domain discriminators and label predictors.
  • �� Applying transferability weights to both source classifiers and domain discriminators.
  • �� Leveraging entropy minimization to utilize unlabeled target examples effectively.

Experiments

Experiments were conducted on Office-31, Office-Home, and ImageNet-Caltech datasets, covering 6, 12, and 2 PDA tasks, respectively. Baselines included DANN, PADA, and IWAN. Metrics like classification accuracy and ablation studies were used to evaluate ETN's components.

Results

ETN achieved 94.5% accuracy on Office-31 A→W, outperforming PADA by 8%. On Office-Home, it averaged 71.9% accuracy across 12 tasks, surpassing IWAN by 10.6%. Ablation studies confirmed the importance of the weighting mechanism and auxiliary modules.

Applications

ETN is applicable to cross-domain classification tasks, such as transferring knowledge from large labeled datasets (e.g., ImageNet) to specialized domains (e.g., medical imaging). Its weighting mechanism can also generalize to other transfer learning scenarios.

Limitations & Outlook

ETN's main limitations include high computational complexity, sensitivity to hyperparameters, and potential underperformance with very limited target domain samples. Future work could address these challenges through algorithmic optimization and regularization techniques.

Plain Language Accessible to non-experts

Imagine you're in a massive library with thousands of books (source domain), but you only need a few specific ones (target domain). ETN acts like a smart librarian, identifying the books you need based on your interests (target label space) and ignoring irrelevant ones. It uses a 'weight' system to decide which books are most important and ensures you only focus on the right ones, saving you time and effort.

ELI14 Explained like you're 14

Think of it like playing a treasure hunt game. There's a huge map with lots of treasures (source domain), but you only need a few specific ones (target domain). ETN is like a super-smart helper that marks the treasures you need and avoids the fake ones. It even helps you figure out the best way to collect them. Cool, right?

Glossary

Partial Domain Adaptation

A transfer learning method where the target domain's label space is a subset of the source domain.

ETN addresses negative transfer in this scenario using a weighting mechanism.

Domain-Invariant Features

Features that are consistent across source and target domains, facilitating transfer learning.

ETN learns these features using adversarial training.

Negative Transfer

The degradation of performance caused by transferring irrelevant knowledge from the source domain.

ETN mitigates this by filtering out irrelevant source examples.

Transferability Weighting

A mechanism to quantify the importance of source examples for the target domain task.

ETN uses this to prioritize relevant examples and ignore irrelevant ones.

Auxiliary Domain Discriminator

A module that evaluates the transferability of source examples by combining domain and label information.

ETN uses this to enhance weight discrimination.

Open Questions Unanswered questions from this research

  • 1 How can ETN be adapted for open-set domain adaptation scenarios?
  • 2 What strategies can improve ETN's performance with extremely limited target domain samples?
  • 3 How can ETN's computational complexity be reduced for large-scale datasets?

Applications

Immediate Applications

Cross-Domain Image Classification

Transfer knowledge from large labeled datasets (e.g., ImageNet) to smaller, domain-specific datasets like medical imaging.

Bioinformatics

Predict protein functions for new species using large databases of known protein structures.

Long-term Vision

Universal Transfer Learning Systems

Develop frameworks that automatically adapt to various domains with minimal human intervention.

Abstract

Domain adaptation is critical for learning in new and unseen environments. With domain adversarial training, deep networks can learn disentangled and transferable features that effectively diminish the dataset shift between the source and target domains for knowledge transfer. In the era of Big Data, the ready availability of large-scale labeled datasets has stimulated wide interest in partial domain adaptation (PDA), which transfers a recognizer from a labeled large domain to an unlabeled small domain. It extends standard domain adaptation to the scenario where target labels are only a subset of source labels. Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer. In this work, we propose a unified approach to PDA, Example Transfer Network (ETN), which jointly learns domain-invariant representations across the source and target domains, and a progressive weighting scheme that quantifies the transferability of source examples while controlling their importance to the learning task in the target domain. A thorough evaluation on several benchmark datasets shows that our approach achieves state-of-the-art results for partial domain adaptation tasks.

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