Research map / Architectures and efficiency / CNN design

Mind the Pool: Convolutional Neural Networks Can Overfit Input Size

ICLR 2023 · Architectures and efficiency › CNN design · approach: Efficient CNN scaling and dynamic inference

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Closest papers by meaning

• Detail-Preserving Pooling in Deep Networks (CVPR 2018 · similarity 0.88)
• TI-Pooling: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks (CVPR 2016 · similarity 0.87)
• Inductive Bias of Deep Convolutional Networks through Pooling Geometry (ICLR 2017 · similarity 0.87)
• S3Pool: Pooling With Stochastic Spatial Sampling (CVPR 2017 · similarity 0.87)
• Gaussian-Based Pooling for Convolutional Neural Networks (NeurIPS 2019 · similarity 0.87)
• Beyond Skip Connection: Pooling and Unpooling Design for Elimination Singularities (AAAI 2025 · similarity 0.85)