Nov 1, 2021 · We propose an adaptive and exponentially weighted pooling method: adaPool. Our method learns a regional-specific fusion of two sets of pooling kernels.

Dec 21, 2022 · Their goal is to produce downsampled volumes that closely resemble the input volume while, ideally, also being computationally and memory ...

We propose an adaptive and exponentially weighted pooling method: adaPool. Our method learns a regional-specific fusion of two sets of pooling kernels.

This work proposes an adaptive and exponentially weighted pooling method that improves the preservation of detail on a range of tasks including image and ...

Sep 8, 2024 · AdaPool improves the preservation of detail on a range of tasks including image and video classification and object detection. A key property of ...

A key property of adaPool is that gradients are calculated for each kernel vector during backpropagation. This improves the network connectivity. In addition, ...

Nov 2, 2021 · Abstract—Pooling layers are essential building blocks of Con- volutional Neural Networks (CNNs) that reduce computational.

A key property of adaPool is its bidirectional nature. In contrast to common pooling methods, the learned weights can also be used to upsample activation maps.

Nov 1, 2021 · We propose an adaptive and exponentially weighted pooling method named adaPool. Our proposed method uses a parameterized fusion of two sets of pooling kernels.

To this end, we propose an adaptive and exponentially weighted pooling method: adaPool. Our method learns a regional-specific fusion of two sets of pooling ...