# Demystifying and Generalizing BinaryConnect

Source: [Demystifying and Generalizing BinaryConnect](<https://www.sota2.com/research/paper/68fa6b1c1e209131cea2c4b4>)

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

Data updated: 2026-01-01T13:54:45.144Z

Authors: Tim Dockhorn, Yaoliang Yu, Eyy\\"ub Sari, Mahdi Zolnouri, Vahid Partovi Nia

Published: 2021-10-25T19:07:38.000Z

## Abstract

BinaryConnect (BC) and its many variations have become the de facto standard for neural network quantization. However, our understanding of the inner workings of BC is still quite limited. We attempt to close this gap in four different aspects: (a) we show that existing quantization algorithms, including post-training quantization, are surprisingly similar to each other; (b) we argue for proximal maps as a natural family of quantizers that is both easy to design and analyze; (c) we refine the observation that BC is a special case of dual averaging, which itself is a special case of the generalized conditional gradient algorithm; (d) consequently, we propose ProxConnect (PC) as a generalization of BC and we prove its convergence properties by exploiting the established connections. We conduct experiments on CIFAR-10 and ImageNet, and verify that PC achieves competitive performance.

## 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.

- [Image Classification on ImageNet (test)](<https://www.sota2.com/research/sota/image-classification-on-imagenet-test.md>): Image Classification; ImageNet (test); Top-1 Acc: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2110.13220>)

[PDF](<https://arxiv.org/pdf/2110.13220.pdf>)