Online Class-Incremental SAR Target Recognition With Interference-Aware Replay
Bibliographic record
Abstract
To address the challenge of catastrophic forgetting in synthetic aperture radar (SAR) image recognition caused by viewpoint-sensitive, high-interference samples encountered in dynamic environments, we propose a lightweight and efficient online class-incremental learning (OCI) framework named Interference-Aware Replay with Dynamic Review for SAR Target Recognition (IAR-DR). Based on the experience replay (ER) mechanism, a Maximally Interfered Retrieval (MIR) strategy is designed to prioritize the replay of high-interference samples by measuring loss changes before and after model updates, thereby preserving decision boundaries under viewpoint variation. A Review Trick (RT) mechanism is further introduced to periodically revisit all buffered samples with a low learning rate, which complements MIR by reinforcing global feature retention and enhancing long-term memory stability. The combination of MIR and RT achieves a synergistic balance between local discrimination and global generalization, mitigating the forgetting effect while maintaining the efficiency. Extensive experiments conducted on the MSTAR and Bistatic MiniSAR datasets demonstrate that the proposed IAR-DR framework maintains high recognition accuracy while achieving a forgetting rate as low as 6.92% in ablation studies, and improving retention by 4.7% over recent SAR class-incremental methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".