Evaluating Post-training Compression In Gans Using Locality-sensitive Hashing

Abstract

The analysis of the compression effects in generative adversarial networks (GANs) after training, i.e. without any fine-tuning, remains an unstudied, albeit important, topic with the increasing trend of their computation and memory requirements. While existing works discuss the difficulty of compressing GANs during training, requiring novel methods designed with the instability of GANs training in mind, we show that existing compression methods (namely clipping and quantization) may be directly applied to compress GANs post-training, without any additional changes. High compression levels may distort the generated set, likely leading to an increase of outliers that may negatively affect the overall assessment of existing k-nearest neighbor (KNN) based metrics. We propose two new precision and recall metrics based on locality-sensitive hashing (LSH), which, on top of increasing the outlier robustness, decrease the complexity of assessing an evaluation sample against \(n\) reference samp

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keymordido2021evaluating

Related papers

• Deep Semantic Hashing With Generative Adversarial Networks (2018)13.50
• Regularizing Deep Hashing Networks Using GAN Generated Fake Images (2018)0.00
• Drop The GAN: In Defense Of Patches Nearest Neighbors As Single Image Generative Models (2021)12.87
• Bingan: Learning Compact Binary Descriptors With A Regularized GAN (2018)0.00
• Sparse-inductive Generative Adversarial Hashing For Nearest Neighbor Search (2023)0.00
• Hashgan:attention-aware Deep Adversarial Hashing For Cross Modal Retrieval (2017)15.34
• SCH-GAN: Semi-supervised Cross-modal Hashing By Generative Adversarial Network (2018)15.03
• Embedding Compression With Hashing For Efficient Representation Learning In Large-scale Graph (2022)8.60