Tensor-empowered Incomplete Multimodal Learning with Modality Reconstruction for Edge Intelligence
Bibliographic record
Abstract
The distributed computing paradigm of edge computing effectively addresses the challenges of data transmission delay and data privacy security. With the increasing popularity of IoT devices and 5 G networks, edge computing has a broader range of applications. The advancement in AI technology enables the realization of edge intelligence, which conducts data processing and analysis on edge devices to avoid excessive data transmission to the cloud, enhance system response speed, and protect user data privacy. In various edge intelligent systems like smart homes and autonomous driving, multimodal data plays a crucial role. However, missing modalities in such systems may lead to model failure in real-world environments. To tackle this issue, we propose a tensor-empowered modality reconstruction network (TMRN) that utilizes an end-to-end variational autoencoder for reconstructing missing modal data. This approach effectively enhances model robustness while reducing model size and training complexity. Furthermore, we introduce a supervised method for feature reconstruction to better align with the true distribution of missing modal data by leveraging tensor feature fusion and label supervision techniques. Additionally, we design a task information disentanglement module to make multimodal representations more relevant to specific tasks by effectively separating task-relevant from task-irrelevant information. Extensive experiments demonstrate that TMRN achieves competitive performance compared to existing state-of-the-art methods.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".