# Deep Learning: Generalization Requires Deep Compositional Feature Space Design

Source: [Deep Learning: Generalization Requires Deep Compositional Feature Space Design](<https://www.sota2.com/research/paper/68fa63831e209131ce9ff180>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:42:16.909Z

Authors: Mrinal Haloi

Published: 2017-06-06T21:10:07.000Z

## Abstract

Generalization error defines the discriminability and the representation power of a deep model. In this work, we claim that feature space design using deep compositional function plays a significant role in generalization along with explicit and implicit regularizations. Our claims are being established with several image classification experiments. We show that the information loss due to convolution and max pooling can be marginalized with the compositional design, improving generalization performance. Also, we will show that learning rate decay acts as an implicit regularizer in deep model training.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1706.01983>)

[PDF](<https://arxiv.org/pdf/1706.01983.pdf>)