DINE: Domain Adaptation from Single and Multiple Black-box Predictors (2104.01539v3)

Abstract: To ease the burden of labeling, unsupervised domain adaptation (UDA) aims to transfer knowledge in previous and related labeled datasets (sources) to a new unlabeled dataset (target). Despite impressive progress, prior methods always need to access the raw source data and develop data-dependent alignment approaches to recognize the target samples in a transductive learning manner, which may raise privacy concerns from source individuals. Several recent studies resort to an alternative solution by exploiting the well-trained white-box model from the source domain, yet, it may still leak the raw data through generative adversarial learning. This paper studies a practical and interesting setting for UDA, where only black-box source models (i.e., only network predictions are available) are provided during adaptation in the target domain. To solve this problem, we propose a new two-step knowledge adaptation framework called DIstill and fine-tuNE (DINE). Taking into consideration the target data structure, DINE first distills the knowledge from the source predictor to a customized target model, then fine-tunes the distilled model to further fit the target domain. Besides, neural networks are not required to be identical across domains in DINE, even allowing effective adaptation on a low-resource device. Empirical results on three UDA scenarios (i.e., single-source, multi-source, and partial-set) confirm that DINE achieves highly competitive performance compared to state-of-the-art data-dependent approaches. Code is available at \url{https://github.com/tim-learn/DINE/}.

Analysis of "DINE: Domain Adaptation from Single and Multiple Black-box Predictors"

The research paper, "DINE: Domain Adaptation from Single and Multiple Black-box Predictors," explores an unsupervised domain adaptation (UDA) scenario where only black-box models are available from the source domain(s). A black-box model implies that only the outputs (predictions) are accessible, not the model’s architecture or the source data. This situation presents challenges regarding privacy preservation and computational efficiency, as opposed to traditional UDA methods that typically require full access to source data or model parameters for domain adaptation.

Problem Statement

A critical challenge in UDA is transferring knowledge from one or more source domains to an unlabeled target domain without violating data privacy restrictions. Many existing UDA methods access raw source data, which is not always feasible due to privacy concerns, especially in sensitive domains such as healthcare. Even when utilizing well-trained white-box models from the source domain, data reconstruction risks persist if adversarial generative techniques are employed. The paper proposes addressing UDA with only black-box access to the source models, preserving data privacy and reducing dependency on device capabilities.

Proposed Method: DINE

The paper introduces a novel two-step knowledge adaptation framework called DIstill and fine-tuNE (DINE), suitable for scenarios where only black-box source model predictions are provided. DINE is structured as follows:

1. Distillation Phase: DINE first distills the knowledge from source predictions to a target model. This distillation process involves an adaptive label smoothing technique that refines source predictions to suit the target model. The process incorporates:
   • Adaptive Label Smoothing: By retaining top-r predictions and averaging others, this technique focuses the distillation process, making it robust against noisy predictions.
   • Structural Regularizations: The use of interpolation consistency training and mutual information maximization adds batch-wise and global structure-awareness to the target model, enabling better generalization.
2. Fine-tuning Phase: Once the target model is distilled, DINE performs fine-tuning using the target domain's data structure, further aligning the model’s predictions with the target domain characteristics.

Empirical Evaluation

The empirical analysis spans multiple scenarios across various datasets (Office, Office-Home, VisDA-C), testing the performance of DINE in single-source, multi-source, and partial-set UDA scenarios. The results show that DINE consistently achieves competitive accuracy compared to state-of-the-art data-dependent and model-based UDA approaches. Particularly of note, DINE maintains strong performance even when distilling knowledge from large architecture like ViT to smaller target architectures, showing the flexibility and efficiency of the method.

Implications and Future Directions

This method addresses two significant issues in UDA: privacy concerns from data sharing and computational limitations on target devices. DINE's introduction of structural regularizations into the distillation process for unsupervised learning stands out as a methodological contribution and could inspire further exploration into structural learning paradigms within UDA contexts.

Future advancements might explore optimizing target model selection automatically based on target domain characteristics or extend this approach to domain adaptation scenarios involving unknown or additional target classes. Additionally, further integrating adversarial robustness to ensure the model's soundness against potential structural shifts in prediction could provide more resilience and accuracy.

In conclusion, DINE offers a robust, privacy-conscious approach to domain adaptation using solely black-box predictors, pushing the boundaries in scenarios where data privacy and computational efficiency are paramount.