Proposes Two-Weighted Activation Mapping (TWAM) in an Attention-based MIL framework guided by saliency maps that outperforms prior weakly supervised methods by at least 16 points on Citrus Pest and Insect Pest benchmarks while producing location maps without bounding-box labels.
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DenoGrad refines noisy tabular and time-series data by optimizing inputs via gradients from a fixed model, yielding better downstream predictions on ten real-world datasets while preserving data statistics.
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Weakly Supervised Attention-based Models Using Activation Maps for Citrus Mite and Insect Pest Classification
Proposes Two-Weighted Activation Mapping (TWAM) in an Attention-based MIL framework guided by saliency maps that outperforms prior weakly supervised methods by at least 16 points on Citrus Pest and Insect Pest benchmarks while producing location maps without bounding-box labels.
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DenoGrad: A Gradient-Based Framework for Data Refinement in Tabular and Time-Series Learning
DenoGrad refines noisy tabular and time-series data by optimizing inputs via gradients from a fixed model, yielding better downstream predictions on ten real-world datasets while preserving data statistics.