ADRM
Adaptive Dynamic Recursive Mapping
**Adaptive Dynamic Recursive Mapping (ADRM)**  

$$
s_{t+1}=\psi_{\alpha}(s_t)=s_t+\alpha\,(s_t-s_t^{2}),\quad \alpha\in[-1,1].
$$  

- $\alpha>0$ amplifies evidence for abnormality  
- $\alpha<0$ suppresses false positives  
- $\alpha=0$ leaves the score unchanged  

Here, $s_t$ is the anomaly score at step $t$, and the adaptive decision parameter $\alpha$ is learned jointly with the backbone detector. By recursively mapping the score trajectory, ADRM stabilises detector outputs, magnifies truly anomalous segments, and damps noisy spikes, yielding more reliable video-level anomaly detection under weak supervision and heterogeneous federated settings.
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping
https://paperswithcode.com/paper/dual-detector-re-optimization-for-federated
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping