Deep UCSL uses a contrastive EM loss on patient-control labels to isolate disease-driven subgroups in medical imaging by suppressing shared healthy variability.
In: International Conference on Learning Representations (ICLR) (2021)
3 Pith papers cite this work. Polarity classification is still indexing.
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DETOUR enables practical backdoor attacks on object detectors by training with rescaled semantic triggers from real-world objects placed at multiple locations to exploit the trigger radiating effect for reliable activation under varying fields of view and spatial configurations.
Strategic insertion of Global Average Pooling layers in VGG-16 reduces trainable parameters by 98%, maintains 66.4% ImageNet Top-1 accuracy, doubles translation robustness, and yields superior Spearman correlations in perceptual IQA tasks.
citing papers explorer
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Automatic Discovery of Disease Subgroups by Contrasting with Healthy Controls
Deep UCSL uses a contrastive EM loss on patient-control labels to isolate disease-driven subgroups in medical imaging by suppressing shared healthy variability.
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DETOUR: A Practical Backdoor Attack against Object Detection
DETOUR enables practical backdoor attacks on object detectors by training with rescaled semantic triggers from real-world objects placed at multiple locations to exploit the trigger radiating effect for reliable activation under varying fields of view and spatial configurations.
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Parameter-Efficient Architectural Modifications for Translation-Invariant CNNs
Strategic insertion of Global Average Pooling layers in VGG-16 reduces trainable parameters by 98%, maintains 66.4% ImageNet Top-1 accuracy, doubles translation robustness, and yields superior Spearman correlations in perceptual IQA tasks.