An innovative meta-learning framework augmented by uncertainty generation is proposed that improves the current state-of-the-art by an average of 3.11%, with key innovations in using probabilistic feature statistics, gradient-based uncertainty modeling, and causal feature extraction that enhance cross-domain knowledge transfer more effectively than existing techniques.
Abstract
Cross-domain few-shot learning focuses on transferring knowledge from a source domain to an unseen target domain, but traditional methods often rely on simple additive perturbations, which limit their effectiveness in handling domain shifts. To address this limitation, we propose an innovative meta-learning framework augmented by uncertainty generation. Our approach introduces a novel gradient-based uncertainty generation method that treats feature statistics, such as mean and variance, as probabilistic representations assuming an unknown distribution. These statistics are manipulated through task gradients, which allow the model to capture the range of possible variations, thereby generating uncertainty feature distributions that simulate domain biases. Additionally, we incorporate a causal invariant information (CII) module to extract causal features and improve the consistency of task gradients across domains. This module ensures that the task gradients reflect domain-invariant features, reducing the impact of domain-specific noise and providing a more reliable framework for constructing uncertainty boundaries. Together, these components enable the development of an asymptotic meta-learning optimization algorithm that learns generalized knowledge from uncertainty features while optimizing the uncertainty distribution boundaries. Experimental results across nine datasets demonstrate that our method improves the current state-of-the-art by an average of 3.11%, with key innovations in using probabilistic feature statistics, gradient-based uncertainty modeling, and causal feature extraction that enhance cross-domain knowledge transfer more effectively than existing techniques.
Meta-learning has emerged as an effective solver for cross-domain few-shot learning (CD-FSL) tasks. Despite achieving obvious progress recently, the typical episodic learning paradigm often causes the feature embedding model collapsing into the simplicity bias pitfall, viz., the model tends to prioritize some shortcut patterns (e.g., color, style, background) that are only sufficient to distinguish categories in source domain, while fail to generalize across domains. To mitigate this problem, we present a novel meta-learning framework which emphasizes meta-exploiting inductive bias to alleviate simplicity bias for CD-FSL promotion, and mainly contributes in the following four aspects. 1) We establish a novel inductive bias for CD-FSL, termed complementary semantic consistency (CSC). The rationale behind lies in that forcing the semantic consistency between two complementary feature learning schemes is beneficial to distill cross-domain transferable features. 2) We establish a solid theoretical foundation, supported by rigorous mathematical proofs and key lemmas, which demonstrates that CSC establishes a tighter generalization bound and facilitates the learning of domain-invariant features. 3) Inspired by CSC, we propose a general meta-learning framework, which implements complementary feature embedding models using parallel networks with the same architecture but different input forms, and introduce proper knowledge distillation losses to encourage the semantic consistency between different branches during meta-training. This framework can be seamlessly integrated with any complementary feature learning schemes. 4) To clarify this point, we instantiate two effective meta-learners based on the proposed framework. The former establishes a two-branch network that simultaneously classifies both the query image and its random local crops. The latter decomposes the query image into high-frequency and low-frequency components, which are then integrated into a parallel feature embedding network for category prediction, analogous to the original query image. Subsequently, a KL divergence based knowledge distillation loss is separately leveraged to force the prediction consistency between the complementary branches (e.g., local-global, spatial-frequency) during meta-training. By doing these, both learners are able to distill cross-domain transferable features with better generalization performance. Empirical results on diverse benchmarks consistently affirm the proposed framework’s advantages, while additional analysis provides compelling support for our key claims.
Fei Zhou, Peng Wang, Lei Zhang et al.· IEEE Transactions on Image P...· 0 citations
Active Domain Adaptation (ADA) enhances transfer learning by selecting the most informative samples from the target domain for annotation. The source-free variant, ASFDA, operates using only a pre-trained source model and unlabeled target data. However, existing approaches typically assume that distribution shift arises solely from changes in the feature or label distribution, thereby neglecting concept shift (the divergence of $p(y|x)$ across domains). In this paper, we propose ADA-CS, a plug-and-play module compatible with any ADA or ASFDA framework. Without requiring access to source data, ADA-CS isolates concept shift from other forms of shifts. The concept shift severity is quantified to prioritize samples exhibiting the largest discrepancy in $p(y|x)$ for annotation. We further introduce a CSS metric to quantify the Concept Shift Severity across domains, revealing that non-negligible concept shift exists in many transfer tasks. Extensive experiments on four vision benchmarks (Office-31, Digits, DomainNet, and Office-Home RSUT) and one large-scale tabular dataset (USAccident) show that ADA-CS improves performance when combined with a wide range of active learning, ADA, and ASFDA strategies. Moreover, the rapid decline of CSS during adaptation provides direct evidence of our approach's effectiveness in identifying and correcting concept shift.
Although SDG methods improve performance under highly domain distinguishable stylized shifts, they exhibit limited robustness to background, correlation and corruption shifts on larger datasets, and increased shape bias does not consistently yield enhanced OOD performance, thereby underscoring the need for further research into developing more resilient and generalizable models.
K. Imbulgoda, Ruwan Tennakoon, W. Chuah et al.· International Journal of Com...· 0 citations
Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples. This setting is more realistic than typical domain adaptation setups, which assume access to target data during source training. However, prior FSTT-DA approaches fail to effectively leverage source domain-specific knowledge, relying on shallow batch normalization updates, prompt-based methods that treat the model as a black box, or ensembling strategies that do not capture cross-domain relationships. To address these limitations, we introduce a new FSTT-DA framework that integrates LoRA fine-tuning with model merging. In our approach, separate LoRA modules are fine-tuned on CLIP's vision encoder for each source domain. Since LoRA modifies only a small fraction of the model's parameters, it retains the base model's generalized knowledge while internally learning domain-specific features. To adapt the learned knowledge to a specific target domain, we propose a hypernetwork trained via meta-learning that generates per-column merging factors to combine LoRA modules. Given a small batch of target images, the hypernetwork produces merging weights that fuse source LoRA modules into a single adapted representation. Our results demonstrate state-of-the-art performance across various domain adaptation datasets. Our code is publicly available at https://github.com/nahbois4321/DA-MergeLoRA.
Siobhan Reid, Zhixiang Chi, Li Gu et al.· 0 citations
Efficient Unsupervised Domain Adaptation (EUDA) is proposed, a parameter-efficient framework that leverages a frozen DINOv2 backbone as a feature extractor and updates only a lightweight bottleneck and classification head to promote both discriminative learning and cross-domain alignment.
Ali Abedi, Q. M. J. Wu, Ning Zhang et al.· International Journal of Mac...· 9 citations
Generative models trained on a source domain often produce samples that are poorly aligned with shifted target domains, limiting their effectiveness for target-domain data augmentation. Although target-specific adaptation can reduce this mismatch, it typically requires additional optimization and domain-specific parameters. We propose a Similarity-based Generative Network (SGN), a reusable framework that is trained once on labeled source data and applied to new target domains without parameter updates. SGN learns a latent space structured by label-induced pairwise similarities while preserving reconstructive information through an encoder-decoder architecture. At generation time, a small labeled representative set from the target domain is encoded and combined in the learned latent space, allowing the generated samples to inherit target-specific characteristics while maintaining class consistency. We further analyze the realizability and dimensionality requirements of the proposed similarity structure. Experiments on image and tabular datasets demonstrate the effectiveness of SGN for target-guided data augmentation under source-to-target distribution shifts.
Jiaqi Zhu, Xincheng Chen, Yuncheng Wu et al.· 0 citations