cleanup

thesis/Main.tex

@@ -1081,9 +1081,11 @@ While the prior analysis provided valuable insights into the classification capa
 To provide a more intuitive understanding of how the methods might perform in real-world applications, we therefore present results from running inference sequentially on entire experiments.
 These frame-by-frame time-axis plots simulate online inference and illustrate how anomaly scores evolve as data is captured, thereby serving as a candidate metric for quantifying the degree of \rev{LiDAR} degradation during operation.
 
-\fig{results_inference_normal_vs_degraded}{figures/results_inference_normal_vs_degraded.png}{Comparison of anomaly detection methods with statistical indicators across clean (dashed) and degraded (solid) experiments. Each subplot shows one method (DeepSAD--LeNet, DeepSAD--Efficient, OCSVM, Isolation Forest). Red curves denote how strongly the anomaly score deviates from clean-experiment baseline; blue and green curves denote the percentage of missing \rev{LiDAR} points and near-sensor particle hits, respectively. Latent Space Dimensionality was 32 and semi-supervised labeling regime was 0 normal and 0 anomalous samples during training.}
+%\fig{results_inference_normal_vs_degraded}{figures/results_inference_normal_vs_degraded.png}{Comparison of anomaly detection methods with statistical indicators across clean (dashed) and degraded (solid) experiments. Each subplot shows one method (DeepSAD--LeNet, DeepSAD--Efficient, OCSVM, Isolation Forest). Red curves denote how strongly the anomaly score deviates from clean-experiment baseline; blue and green curves denote the percentage of missing \rev{LiDAR} points and near-sensor particle hits, respectively. Latent Space Dimensionality was 32 and semi-supervised labeling regime was 0 normal and 0 anomalous samples during training.}
 
-As discussed in Section~\ref{sec:setup_baselines_evaluation} we apply $z$-score normalization to enable comparison of the different methods during inference.  After normalization, the resulting time series were still highly noisy, which motivated the application of exponential moving average (EMA) smoothing. EMA was chosen because it is causal (does not rely on future data) and thus suitable for real-time inference. Although it introduces a small time delay, this delay is shorter than for other smoothing techniques such as running averages.
+\fig{results_inference_normal_vs_degraded}{figures/results_inference_normal_vs_degraded.png}{Comparison of inference on unseen experiment for clean (dashed) vs. degraded (solid) experiments. Each subplot compares one method to statistical indicators. Red curves show method's anomaly score deviation from its clean baseline; blue and green curves indicate the percentage of missing \rev{LiDAR} points and near-sensor particle hits, respectively. Latent dimension: 32; training regime: 0 normal, 0 anomalous samples.}
+
+As discussed in Section~\ref{sec:setup_baselines_evaluation}, we apply $z$-score normalization to enable comparison of the different methods during inference.  After normalization, the resulting time series were still highly noisy, which motivated the application of exponential moving average (EMA) smoothing. EMA was chosen because it is causal (does not rely on future data) and thus suitable for real-time inference. Although it introduces a small time delay, this delay is shorter than for other smoothing techniques such as running averages.
 
 The plots in Figure~\ref{fig:results_inference_normal_vs_degraded} highlight important differences in how well the tested methods distinguish between normal and degraded sensor conditions. The plots show how strongly the method's scores deviate from their clean-data baseline and include statistical indicators (missing points and near-sensor particle hits) in blue and green.