Research map / Architectures and efficiency / CNN design
TI-Pooling: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TI-pooling over 24 rotations instead of a TIRBM baseline | MNIST rotation classification · mnist-rot-12k | error (%): 4.2 -> 1.2 | Helped |
| Use TI-pooling instead of STN (affine) | Half-rotated MNIST · Half-rotated MNIST | error (%): 0.7 -> 0.8 | Hurt |
| Use TI-pooling instead of CNN with data augmentation | Neuronal segmentation · Neuronal segmentation test | pixel error (%): 8.1 -> 7.0 | Helped |
| Use TI-pooling instead of MIL over CNN | Neuronal segmentation · Neuronal segmentation test | pixel error (%): 8.9 -> 7.0 | Helped |
| Add dropout (ablation) | TI-pooling · Neuronal segmentation test | pixel error (%): 7.4 -> 7.0 | Helped |
| Increase the number of sampled transformations in the TI-pooling set (ablation) | TI-pooling · Neuronal segmentation validation | validation error | Helped |
Closest papers by meaning
- Detail-Preserving Pooling in Deep Networks (CVPR 2018 · similarity 0.88)
- S3Pool: Pooling With Stochastic Spatial Sampling (CVPR 2017 · similarity 0.87)
- Gaussian-Based Pooling for Convolutional Neural Networks (NeurIPS 2019 · similarity 0.87)
- Inductive Bias of Deep Convolutional Networks through Pooling Geometry (ICLR 2017 · similarity 0.87)
- Mind the Pool: Convolutional Neural Networks Can Overfit Input Size (ICLR 2023 · similarity 0.87)
- Beyond Skip Connection: Pooling and Unpooling Design for Elimination Singularities (AAAI 2025 · similarity 0.85)
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