OCO mitigates simplicity bias in OOD detection by predicting disentangled representations, dividing patterns into three co-occurrence scenarios from ID data, and applying divide-and-conquer detection.
Deep residual learning for image recognition
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TAS-LoRA attaches a mixture of LoRA experts to a supernet and uses a dynamic router plus group-wise initialization to let different architecture subnets learn distinct features, yielding higher accuracy than prior TAS methods on ImageNet and transfer datasets.
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Divide and Conquer: Object Co-occurrence Helps Mitigate Simplicity Bias in OOD Detection
OCO mitigates simplicity bias in OOD detection by predicting disentangled representations, dividing patterns into three co-occurrence scenarios from ID data, and applying divide-and-conquer detection.
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TAS-LoRA: Transformer Architecture Search with Mixture-of-LoRA Experts
TAS-LoRA attaches a mixture of LoRA experts to a supernet and uses a dynamic router plus group-wise initialization to let different architecture subnets learn distinct features, yielding higher accuracy than prior TAS methods on ImageNet and transfer datasets.