Zero-Shot Learning of a Conditional Generative Adversarial Network for Data-Free Network Quantization

• URL: http://arxiv.org/abs/2210.14392v1
• Date: Wed, 26 Oct 2022 00:05:57 GMT
• Title: Zero-Shot Learning of a Conditional Generative Adversarial Network for Data-Free Network Quantization
• Authors: Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
• Abstract summary: We propose a novel method for training a conditional generative adversarial network (CGAN) without the use of training data. Zero-shot learning of a conditional generator only needs a pre-trained discriminative (classification) model and does not need any training data. We show the usefulness of ZS-CGAN in data-free quantization of deep neural networks.
• Score: 44.22469647001933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel method for training a conditional generative adversarial network (CGAN) without the use of training data, called zero-shot learning of a CGAN (ZS-CGAN). Zero-shot learning of a conditional generator only needs a pre-trained discriminative (classification) model and does not need any training data. In particular, the conditional generator is trained to produce labeled synthetic samples whose characteristics mimic the original training data by using the statistics stored in the batch normalization layers of the pre-trained model. We show the usefulness of ZS-CGAN in data-free quantization of deep neural networks. We achieved the state-of-the-art data-free network quantization of the ResNet and MobileNet classification models trained on the ImageNet dataset. Data-free quantization using ZS-CGAN showed a minimal loss in accuracy compared to that obtained by conventional data-dependent quantization.

Related papers

• Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation [10.793646707711442]
  We present a framework for training generative models for density estimation. We use the score-based diffusion model to generate labeled data. Once the labeled data are generated, we can train a simple fully connected neural network to learn the generative model in the supervised manner.
  arXiv  Detail & Related papers  (2023-10-22T23:56:19Z)
• Post-training Model Quantization Using GANs for Synthetic Data Generation [57.40733249681334]
  We investigate the use of synthetic data as a substitute for the calibration with real data for the quantization method. We compare the performance of models quantized using data generated by StyleGAN2-ADA and our pre-trained DiStyleGAN, with quantization using real data and an alternative data generation method based on fractal images.
  arXiv  Detail & Related papers  (2023-05-10T11:10:09Z)
• An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training [0.0]
  Generative networks like invertible neural networks(INN) enable a probabilistic unfolding. We introduce the iterative conditional INN(IcINN) for unfolding that adjusts for deviations between simulated training samples and data.
  arXiv  Detail & Related papers  (2022-12-16T19:00:05Z)
• Neural networks trained with SGD learn distributions of increasing complexity [78.30235086565388]
  We show that neural networks trained using gradient descent initially classify their inputs using lower-order input statistics. We then exploit higher-order statistics only later during training. We discuss the relation of DSB to other simplicity biases and consider its implications for the principle of universality in learning.
  arXiv  Detail & Related papers  (2022-11-21T15:27:22Z)
• ClusterQ: Semantic Feature Distribution Alignment for Data-Free Quantization [111.12063632743013]
  We propose a new and effective data-free quantization method termed ClusterQ. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics. We also incorporate the intra-class variance to solve class-wise mode collapse.
  arXiv  Detail & Related papers  (2022-04-30T06:58:56Z)
• Synthesizing Informative Training Samples with GAN [31.225934266572192]
  We propose a novel method to synthesize Informative Training samples with GAN (IT-GAN) Specifically, we freeze a pre-trained GAN model and learn the informative latent vectors that corresponds to informative training samples. Experiments verify that the deep neural networks can learn faster and achieve better performance when being trained with our IT-GAN generated images.
  arXiv  Detail & Related papers  (2022-04-15T15:16:01Z)
• Diverse Sample Generation: Pushing the Limit of Data-free Quantization [85.95032037447454]
  This paper presents a generic Diverse Sample Generation scheme for the generative data-free post-training quantization and quantization-aware training. For large-scale image classification tasks, our DSG can consistently outperform existing data-free quantization methods.
  arXiv  Detail & Related papers  (2021-09-01T07:06:44Z)
• Generative Zero-shot Network Quantization [41.75769117366117]
  Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration. We show that, for high-level image recognition tasks, we can further reconstruct "realistic" images of each category by leveraging intrinsic Batch Normalization (BN) statistics without any training data.
  arXiv  Detail & Related papers  (2021-01-21T04:10:04Z)
• Statistical model-based evaluation of neural networks [74.10854783437351]
  We develop an experimental setup for the evaluation of neural networks (NNs) The setup helps to benchmark a set of NNs vis-a-vis minimum-mean-square-error (MMSE) performance bounds. This allows us to test the effects of training data size, data dimension, data geometry, noise, and mismatch between training and testing conditions.
  arXiv  Detail & Related papers  (2020-11-18T00:33:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.