ErGAN: Generative Adversarial Networks for Entity Resolution
- URL: http://arxiv.org/abs/2012.10004v1
- Date: Fri, 18 Dec 2020 01:33:58 GMT
- Title: ErGAN: Generative Adversarial Networks for Entity Resolution
- Authors: Jingyu Shao, Qing Wang, Asiri Wijesinghe, Erhard Rahm
- Abstract summary: A major challenge in learning-based entity resolution is how to reduce the label cost for training.
We propose a novel deep learning method, called ErGAN, to address the challenge.
We have conducted extensive experiments to empirically verify the labeling and learning efficiency of ErGAN.
- Score: 8.576633582363202
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Entity resolution targets at identifying records that represent the same
real-world entity from one or more datasets. A major challenge in
learning-based entity resolution is how to reduce the label cost for training.
Due to the quadratic nature of record pair comparison, labeling is a costly
task that often requires a significant effort from human experts. Inspired by
recent advances of generative adversarial network (GAN), we propose a novel
deep learning method, called ErGAN, to address the challenge. ErGAN consists of
two key components: a label generator and a discriminator which are optimized
alternatively through adversarial learning. To alleviate the issues of
overfitting and highly imbalanced distribution, we design two novel modules for
diversity and propagation, which can greatly improve the model generalization
power. We have conducted extensive experiments to empirically verify the
labeling and learning efficiency of ErGAN. The experimental results show that
ErGAN beats the state-of-the-art baselines, including unsupervised,
semi-supervised, and unsupervised learning methods.
Related papers
- Accelerating exploration and representation learning with offline
pre-training [52.6912479800592]
We show that exploration and representation learning can be improved by separately learning two different models from a single offline dataset.
We show that learning a state representation using noise-contrastive estimation and a model of auxiliary reward can significantly improve the sample efficiency on the challenging NetHack benchmark.
arXiv Detail & Related papers (2023-03-31T18:03:30Z) - Conservative Generator, Progressive Discriminator: Coordination of
Adversaries in Few-shot Incremental Image Synthesis [34.27851973031995]
We study the underrepresented task of generative incremental few-shot learning.
We propose a novel framework named ConPro that leverages the two-player nature of GANs.
We present experiments to validate the effectiveness of ConPro.
arXiv Detail & Related papers (2022-07-29T06:00:29Z) - FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity
in Data-Efficient GANs [24.18718734850797]
Data-Efficient GANs (DE-GANs) aim to learn generative models with a limited amount of training data.
Contrastive learning has shown the great potential of increasing the synthesis quality of DE-GANs.
We propose FakeCLR, which only applies contrastive learning on fake samples.
arXiv Detail & Related papers (2022-07-18T14:23:38Z) - Adversarial Dual-Student with Differentiable Spatial Warping for
Semi-Supervised Semantic Segmentation [70.2166826794421]
We propose a differentiable geometric warping to conduct unsupervised data augmentation.
We also propose a novel adversarial dual-student framework to improve the Mean-Teacher.
Our solution significantly improves the performance and state-of-the-art results are achieved on both datasets.
arXiv Detail & Related papers (2022-03-05T17:36:17Z) - Self-Ensembling GAN for Cross-Domain Semantic Segmentation [107.27377745720243]
This paper proposes a self-ensembling generative adversarial network (SE-GAN) exploiting cross-domain data for semantic segmentation.
In SE-GAN, a teacher network and a student network constitute a self-ensembling model for generating semantic segmentation maps, which together with a discriminator, forms a GAN.
Despite its simplicity, we find SE-GAN can significantly boost the performance of adversarial training and enhance the stability of the model.
arXiv Detail & Related papers (2021-12-15T09:50:25Z) - Discriminative-Generative Representation Learning for One-Class Anomaly
Detection [22.500931323372303]
We propose a self-supervised learning framework combining generative methods and discriminative methods.
Our method significantly outperforms several state-of-the-arts on multiple benchmark data sets.
arXiv Detail & Related papers (2021-07-27T11:46:15Z) - Exploring DeshuffleGANs in Self-Supervised Generative Adversarial
Networks [0.0]
We study the contribution of a self-supervision task, deshuffling of the DeshuffleGANs in the generalizability context.
We show that the DeshuffleGAN obtains the best FID results for several datasets compared to the other self-supervised GANs.
We design the conditional DeshuffleGAN called cDeshuffleGAN to evaluate the quality of the learnt representations.
arXiv Detail & Related papers (2020-11-03T14:22:54Z) - Towards Accurate Knowledge Transfer via Target-awareness Representation
Disentanglement [56.40587594647692]
We propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED)
TRED disentangles the relevant knowledge with respect to the target task from the original source model and used as a regularizer during fine-tuning the target model.
Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average.
arXiv Detail & Related papers (2020-10-16T17:45:08Z) - Unsupervised Controllable Generation with Self-Training [90.04287577605723]
controllable generation with GANs remains a challenging research problem.
We propose an unsupervised framework to learn a distribution of latent codes that control the generator through self-training.
Our framework exhibits better disentanglement compared to other variants such as the variational autoencoder.
arXiv Detail & Related papers (2020-07-17T21:50:35Z) - Semi-Supervised StyleGAN for Disentanglement Learning [79.01988132442064]
Current disentanglement methods face several inherent limitations.
We design new architectures and loss functions based on StyleGAN for semi-supervised high-resolution disentanglement learning.
arXiv Detail & Related papers (2020-03-06T22:54:46Z)
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.