FlexPooling with Simple Auxiliary Classifiers in Deep Networks
In computer vision, the basic pipeline of most convolutional neural networks consists of multiple feature extraction layers, where the input signal is downsampled to a lower resolution in each subsequent layer. This downsampling process is commonly referred to as pooling, which is an essential operation in CNNs. Pooling improves robustness against transformations, reduces the number of trainable parameters, increases the receptive field, and lowers computation time. Since pooling is a lossy process but remains important for extracting high-level information from low-level representations, it is important to preserve the most prominent information from previous activations to improve netwo...
| Authors | Muhammad Ali, Omar Alsuwaidi, Salman Khan |
|---|---|
| Primary category | cs.CV |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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