Adaptive Neural Network Architectures for Cross-Domain Generalization
Bibliographic record
Abstract
Cross-domain generalization remains a critical challenge in the field of machine learning. Traditional models often struggle to maintain performance when applied to new, unseen domains due to the variations in data distribution, known as domain shift. This paper proposes adaptive neural network architectures that dynamically adjust their structure based on the domain of the input data. Our approach leverages dynamic routing, attention mechanisms, and modular neural networks to enhance the model's adaptability and robustness. The dynamic routing mechanism enables the network to select different paths for different inputs, allowing it to adapt its processing dynamically. Attention mechanisms help the model focus on the most relevant parts of the input data, enhancing its ability to generalize across domains. Modular neural networks consist of multiple independent modules that can be selectively activated or deactivated based on the input domain. We also develop a dynamic adaptation mechanism that adjusts the network structure in real-time based on domain-specific input features. Experimental results on multiple benchmark datasets, including NEU-CLS and Lithium Electronic Surface Defect Classification (IESDC) datasets, demonstrate the effectiveness of our method. The proposed approach shows significant improvements in cross-domain performance compared to state-of-the-art models, achieving higher accuracy and robustness. Ablation studies confirm the contribution of each component to the overall performance enhancement. The findings highlight the potential of adaptive architectures in addressing the challenges of domain shift in machine learning applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".