Hi Alexis, Christos, Simon I’ve been digging into the TIRAuxCloud codebase and paper following your last broadcast. I'm focusing my GSoC proposal on the XAI and uncertainty quantification extensions you requested. I wanted to ask a quick technical question regarding the "unimodal dominance" issue you highlighted in Section IV. Since the TIR band is so closely correlated with the cloud mask, it provides a simpler, high-signal pathway. It makes complete sense that the network neglects the more complex multimodal relationships found in the DEM and meteorological variables. My proposed architectural solution is auxiliary-attribution regularization: using DeepSHAP (computed periodically on a validation subset) to monitor per-channel feature attribution during training. If auxiliary channels (DEM, meteorological variables) show near-zero attribution while TIR dominates, a regularization term would penalize this imbalance effectively forcing the model to extract signal from the atmospheric context. Combined with input-level modality dropout on TIR, this creates both a push (dropout forces reliance on auxiliaries) and a pull (attribution regularization rewards it) Before I finalize my implementation plan, I wanted to ask: Have you already experimented with any specific architectural interventions like the parallel encoder branches operating at lower spatial frequencies mentioned in your discussion to force the network to utilize the auxiliary data? I'd love to know if any of these directions have already been explored internally, so I can build on your findings rather than spending time on the same. Best regards, Naitik Agrwal >
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