Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks

Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:14096-14113, 2023.

Abstract

This work examines the challenges of training neural networks using vector quantization using straight-through estimation. We find that the main cause of training instability is the discrepancy between the model embedding and the code-vector distribution. We identify the factors that contribute to this issue, including the codebook gradient sparsity and the asymmetric nature of the commitment loss, which leads to misaligned code-vector assignments. We propose to address this issue via affine re-parameterization of the code vectors. Additionally, we introduce an alternating optimization to reduce the gradient error introduced by the straight-through estimation. Moreover, we propose an improvement to the commitment loss to ensure better alignment between the codebook representation and the model embedding. These optimization methods improve the mathematical approximation of the straight-through estimation and, ultimately, the model performance. We demonstrate the effectiveness of our methods on several common model architectures, such as AlexNet, ResNet, and ViT, across various tasks, including image classification and generative modeling.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-huh23a, title = {Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks}, author = {Huh, Minyoung and Cheung, Brian and Agrawal, Pulkit and Isola, Phillip}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {14096--14113}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/huh23a/huh23a.pdf}, url = {https://proceedings.mlr.press/v202/huh23a.html}, abstract = {This work examines the challenges of training neural networks using vector quantization using straight-through estimation. We find that the main cause of training instability is the discrepancy between the model embedding and the code-vector distribution. We identify the factors that contribute to this issue, including the codebook gradient sparsity and the asymmetric nature of the commitment loss, which leads to misaligned code-vector assignments. We propose to address this issue via affine re-parameterization of the code vectors. Additionally, we introduce an alternating optimization to reduce the gradient error introduced by the straight-through estimation. Moreover, we propose an improvement to the commitment loss to ensure better alignment between the codebook representation and the model embedding. These optimization methods improve the mathematical approximation of the straight-through estimation and, ultimately, the model performance. We demonstrate the effectiveness of our methods on several common model architectures, such as AlexNet, ResNet, and ViT, across various tasks, including image classification and generative modeling.} }
Endnote
%0 Conference Paper %T Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks %A Minyoung Huh %A Brian Cheung %A Pulkit Agrawal %A Phillip Isola %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-huh23a %I PMLR %P 14096--14113 %U https://proceedings.mlr.press/v202/huh23a.html %V 202 %X This work examines the challenges of training neural networks using vector quantization using straight-through estimation. We find that the main cause of training instability is the discrepancy between the model embedding and the code-vector distribution. We identify the factors that contribute to this issue, including the codebook gradient sparsity and the asymmetric nature of the commitment loss, which leads to misaligned code-vector assignments. We propose to address this issue via affine re-parameterization of the code vectors. Additionally, we introduce an alternating optimization to reduce the gradient error introduced by the straight-through estimation. Moreover, we propose an improvement to the commitment loss to ensure better alignment between the codebook representation and the model embedding. These optimization methods improve the mathematical approximation of the straight-through estimation and, ultimately, the model performance. We demonstrate the effectiveness of our methods on several common model architectures, such as AlexNet, ResNet, and ViT, across various tasks, including image classification and generative modeling.
APA
Huh, M., Cheung, B., Agrawal, P. & Isola, P.. (2023). Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:14096-14113 Available from https://proceedings.mlr.press/v202/huh23a.html.

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