Robustness Evaluation of Stacked Generative Adversarial Networks using Metamorphic Testing

• URL: http://arxiv.org/abs/2103.02870v1
• Date: Thu, 4 Mar 2021 07:29:17 GMT
• Title: Robustness Evaluation of Stacked Generative Adversarial Networks using Metamorphic Testing
• Authors: Hyejin Park, Taaha Waseem, Wen Qi Teo, Ying Hwei Low, Mei Kuan Lim and Chun Yong Chong
• Abstract summary: StackGAN-v2 has proven capable of generating high resolution images that reflect the details specified in the input text descriptions. We adopt Metamorphic Testing technique to evaluate the robustness of the model with a variety of unexpected training dataset. We find that StackGAN-v2 algorithm is susceptible to input images with obtrusive objects, even if it overlaps with the main object minimally.
• Score: 0.39146761527401414
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Synthesising photo-realistic images from natural language is one of the challenging problems in computer vision. Over the past decade, a number of approaches have been proposed, of which the improved Stacked Generative Adversarial Network (StackGAN-v2) has proven capable of generating high resolution images that reflect the details specified in the input text descriptions. In this paper, we aim to assess the robustness and fault-tolerance capability of the StackGAN-v2 model by introducing variations in the training data. However, due to the working principle of Generative Adversarial Network (GAN), it is difficult to predict the output of the model when the training data are modified. Hence, in this work, we adopt Metamorphic Testing technique to evaluate the robustness of the model with a variety of unexpected training dataset. As such, we first implement StackGAN-v2 algorithm and test the pre-trained model provided by the original authors to establish a ground truth for our experiments. We then identify a metamorphic relation, from which test cases are generated. Further, metamorphic relations were derived successively based on the observations of prior test results. Finally, we synthesise the results from our experiment of all the metamorphic relations and found that StackGAN-v2 algorithm is susceptible to input images with obtrusive objects, even if it overlaps with the main object minimally, which was not reported by the authors and users of StackGAN-v2 model. The proposed metamorphic relations can be applied to other text-to-image synthesis models to not only verify the robustness but also to help researchers understand and interpret the results made by the machine learning models.

Related papers

• Sub-graph Based Diffusion Model for Link Prediction [43.15741675617231]
  Denoising Diffusion Probabilistic Models (DDPMs) represent a contemporary class of generative models with exceptional qualities. We build a novel generative model for link prediction using a dedicated design to decompose the likelihood estimation process via the Bayesian formula. Our proposed method presents numerous advantages: (1) transferability across datasets without retraining, (2) promising generalization on limited training data, and (3) robustness against graph adversarial attacks.
  arXiv  Detail & Related papers  (2024-09-13T02:23:55Z)
• Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection [106.39544368711427]
  We study the problem of generalizable synthetic image detection, aiming to detect forgery images from diverse generative methods. We present a novel forgery-aware adaptive transformer approach, namely FatFormer. Our approach tuned on 4-class ProGAN data attains an average of 98% accuracy to unseen GANs, and surprisingly generalizes to unseen diffusion models with 95% accuracy.
  arXiv  Detail & Related papers  (2023-12-27T17:36:32Z)
• Anomaly Score: Evaluating Generative Models and Individual Generated Images based on Complexity and Vulnerability [21.355484227864466]
  We investigate the relationship between the representation space and input space around generated images. We introduce a new metric to evaluating image-generative models called anomaly score (AS)
  arXiv  Detail & Related papers  (2023-12-17T07:33:06Z)
• Accurate deep learning sub-grid scale models for large eddy simulations [0.0]
  We present two families of sub-grid scale (SGS) turbulence models developed for large-eddy simulation (LES) purposes. Their development required the formulation of physics-informed robust and efficient Deep Learning (DL) algorithms. Explicit filtering of data from direct simulations of canonical channel flow at two friction Reynolds numbers provided accurate data for training and testing.
  arXiv  Detail & Related papers  (2023-07-19T15:30:06Z)
• Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks [0.0]
  We show that precious information is contained in the spectrum of the precision matrix that can be extracted once the training of the model is completed. We conducted numerical experiments for regression, classification, and feature selection tasks. Our results demonstrate that the proposed model does not only yield an attractive prediction performance compared to the competitors.
  arXiv  Detail & Related papers  (2023-07-11T09:54:30Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• MEMO: Test Time Robustness via Adaptation and Augmentation [131.28104376280197]
  We study the problem of test time robustification, i.e., using the test input to improve model robustness. Recent prior works have proposed methods for test time adaptation, however, they each introduce additional assumptions. We propose a simple approach that can be used in any test setting where the model is probabilistic and adaptable.
  arXiv  Detail & Related papers  (2021-10-18T17:55:11Z)
• MOGAN: Morphologic-structure-aware Generative Learning from a Single Image [59.59698650663925]
  Recently proposed generative models complete training based on only one image. We introduce a MOrphologic-structure-aware Generative Adversarial Network named MOGAN that produces random samples with diverse appearances. Our approach focuses on internal features including the maintenance of rational structures and variation on appearance.
  arXiv  Detail & Related papers  (2021-03-04T12:45:23Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)

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.