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Easy Transfer Learning By Exploiting Intra-domain Structures
Jindong Wang, Yiqiang Chen, Han Yu, Meiyu Huang, Qiang Yang
TL;DR
Transfer learning targets similar domains with few or no labels, yet practical use on resource-constrained devices is restricted. EasyTL learns non-parametric transfer features and classifiers from intra-domain structures without model selection or hyperparameter tuning; experiments report superior accuracy and computational efficiency to state-of-the-art traditional and deep methods.
Problem
Transfer learning aims to use a labeled source domain for a similar target with few or no labels, while practical application is restricted on resource-constrained devices.
Source: §1 · p. 1Method
EasyTL exploits intra-domain alignment and programming to learn non-parametric transfer features and classifiers without model selection or hyperparameter tuning.
Source: §1 · p. 2Results
Across visual domain adaptation and cross-domain sentiment analysis, EasyTL showed significant superiority in classification accuracy and computational efficiency over state-of-the-art traditional and deep methods.
Source: §1 · p. 2Takeaways & Limitations
The authors present EasyTL as suitable for resource-constrained devices and as a way to improve existing transfer-learning methods by replacing their classifier.
Source: §1 · p. 2EasyTL addresses how to transfer, not which domain to transfer from; the authors identify automated domain selection as future work.
Source: §5 · p. 6
Abstract
from arXiv · showhide
Transfer learning aims at transferring knowledge from a well-labeled domain to a similar but different domain with limited or no labels. Unfortunately, existing learning-based methods often involve intensive model selection and hyperparameter tuning to obtain good results. Moreover, cross-validation is not possible for tuning hyperparameters since there are often no labels in the target domain. This would restrict wide applicability of transfer learning especially in computationally-constraint devices such as wearables. In this paper, we propose a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance. By exploiting intra-domain structures, EasyTL is able to learn both non-parametric transfer features and classifiers. Extensive experiments demonstrate that, compared to state-of-the-art traditional and deep methods, EasyTL satisfies the Occam's Razor principle: it is extremely easy to implement and use while achieving comparable or better performance in classification accuracy and much better computational efficiency. Additionally, it is shown that EasyTL can increase the performance of existing transfer feature learning methods.
1. INTRODUCTION
Transfer learning can use a labeled source domain to support a similar target domain with few or no labels, but model selection and tuning make practical use challenging. EasyTL exploits intra-domain structures to avoid those steps, with experiments reporting strong accuracy and efficiency.
1. INTRODUCTION
- Existing transfer-learning methods are difficult to apply because model selection and extensive hyperparameter tuning are challenging when target-domain labels are unavailable for cross-validation.The paper also notes that AutoML methods cannot handle differing domain distributions and typically take a long time to converge.Source: §1 · p. 1
- The paper frames this challenge as especially important for resource-constrained devices such as wearables, which require instant local computing.It presents EasyTL as easy to implement and use, accurate, efficient, and extensible.Source: §1 · pp. 1–2
- EasyTL uses intra-domain alignment and programming to learn non-parametric transfer features and a classifier without model selection or hyperparameter tuning.The paper also says intra-domain programming can replace the classifier in existing transfer-learning methods to improve their performance.Source: §1 · p. 2
- Across visual domain adaptation and cross-domain sentiment analysis, EasyTL showed significant superiority in classification accuracy and computational efficiency over state-of-the-art traditional and deep methods.The introduction also describes EasyTL as competitive across popular transfer-learning tasks.Source: §1 · p. 2
2. RELATED WORK
The related-work section contrasts EasyTL with parametric transfer-learning approaches that depend on tuning, and with non-parametric classifiers and AutoML methods that have different constraints.
Transfer learning
- Existing instance-reweighting and feature-transformation methods [23] [17] [20] are parametric and rely on tuning, often across multiple training iterations [20].Deep transfer-learning methods [8] [9] [19] [5] also require heavy hyperparameter tuning, while target-domain labels are often unavailable for cross-validation.Source: §2 · p. 2
Non-parametric learning
- EasyTL uses linear programming to produce softmax probabilities, whereas nearest-centroid classification uses 0/1 weights and nearest-neighbor distances may be affected by domain shift.EasyTL's weights account for sample-to-center relationships and relationships among other samples.Source: §2 · p. 2
Automated machine learning
3. EASY TRANSFER LEARNING
EasyTL addresses transfer between domains with shared feature and label spaces but different distributions, using a classifier that requires no explicit parameter tuning and optional intra-domain alignment. Its programming stage is theoretically comparable in time complexity to one SVM round, while avoiding SVM’s hyperparameter-tuning rounds.
3. EASY TRANSFER LEARNING
- The transfer setting assumes labeled source and unlabeled target domains with shared feature and label spaces but different marginal and conditional distributions.The goal is to predict target-domain labels.Source: §3 · p. 2
3.1. Motivation
- EasyTL targets the tension between simple classifiers that struggle with domain divergence and adaptation methods that require extensive hyperparameter tuning.The paper proposes exploiting intra-domain structure rather than relying only on sample-wise distance.Source: §3.1 · pp. 2–3
3.2. Intra-domain Programming
- Intra-domain programming learns a probability annotation matrix through constrained linear programming, then uses softmax to assign target labels without explicit classifier parameters to tune.Each matrix entry represents a target sample’s probability of belonging to a class; the constraints require probabilities across classes to sum to one and at least one sample per class.Source: §3.2 · p. 3
3.3. Intra-domain Alignment
- Intra-domain alignment can complement EasyTL or existing transfer-feature methods; the CORAL-based option re-colors whitened source features using target covariance.The paper selects CORAL for computational efficiency and because it adds no parameters to tune.Source: §3.3 · p. 4
3.4. Computational Complexity
- Intra-domain programming has time complexity comparable to a single SVM training-and-prediction round, while avoiding SVM’s additional hyperparameter-tuning rounds.The authors therefore describe EasyTL as theoretically more efficient than SVM.Source: §3.4 · p. 4
4. EXPERIMENTAL EVALUATION
Across four transfer-learning benchmarks, EasyTL outperforms comparison methods in accuracy and efficiency, and its classifier can extend existing transfer methods. The evaluation also identifies weaker performance on unbalanced Office-Caltech data.
4.1. Experimental Setup
- Experiments cover Amazon Review, Office-Caltech, Image-CLEF DA, and Office-Home, with any two domains in each dataset usable as source and target.Amazon features are 400-dimensional; image features use 2048-dimensional finetuned ResNet50, except Office-Caltech, which uses SURF features.Source: §4.1 · pp. 4–5
4.2. Results: Accuracy and Efficiency
- EasyTL outperforms comparison methods on sentiment and image data in accuracy and efficiency; EasyTL(c) is competitive with most methods.The reported comparison covers Amazon Review and image datasets; Table 4 reports average rank, parameters, and running time.Source: §4.2 · p. 5, Table 3 · p. 5, Table 2 · p. 5
- On Office-Home, EasyTL(c) is slightly worse than CDAN but outperforms other comparators; EasyTL is limited on unbalanced Office-Caltech.The two-stage finetune-plus-EasyTL procedure requires one distinct finetuning process, while deep methods rerun the network to tune parameters.Source: §4.2 · p. 5
4.3. Evaluation of Extensibility
- Replacing existing methods’ classifiers with EasyTL improves their performance, and EasyTL remains comparable when paired with feature-learning methods other than CORAL.The evaluation compares +SVM and +EasyTL using PCA, TCA, GFK, and CORAL for intra-domain alignment.Source: §4.3 · p. 5, Figure 4 · p. 6
5. CONCLUSIONS AND FUTURE WORK
EasyTL combines non-parametric transfer features and classifiers to avoid model selection and tuning, with experiments reporting superior accuracy and efficiency over several traditional and deep methods. The conclusion also identifies practical transfer learning, domain selection, and multi-label transfer as future directions or extensions.
5. CONCLUSIONS AND FUTURE WORK
- Experiments demonstrate EasyTL’s superiority in accuracy and efficiency over several popular traditional and deep transfer-learning methods.The paper also reports that EasyTL can increase the performance of other transfer-learning methods.Source: §5 · p. 6
- The authors propose practical transfer-learning research that emphasizes actual use, including parameter tuning and model validation, rather than accuracy alone.Source: §5 · p. 6
- EasyTL focuses on how to transfer; deciding what domain to transfer from remains an open problem for future work.The authors note that existing domain-selection approaches also require heavy parameter tuning.Source: §5 · p. 6
- EasyTL can be applied to multi-label transfer by changing the probability annotation matrix, without modifying its classifier.The authors suggest this may make EasyTL advantageous for multi-label tasks.Source: §5 · p. 6