Proposes ACH module with differentiable sampling and softsign normalization for efficient feature expansion, integrated via NAS into Hadaptive-Net to claim SOTA accuracy/speed trade-offs on image classification.
Object Detection - Training Protocol: The base learning rate of 0.02 corresponds to a batch size of 64 distributed across 5 GPUs, scaled linearly according to the batch size
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Expressive yet Efficient Feature Expansion with Adaptive Cross-Hadamard Products
Proposes ACH module with differentiable sampling and softsign normalization for efficient feature expansion, integrated via NAS into Hadaptive-Net to claim SOTA accuracy/speed trade-offs on image classification.