Biased Attention: Do Vision Transformers Amplify Gender Bias More than Convolutional Neural Networks?

• URL: http://arxiv.org/abs/2309.08760v1
• Date: Fri, 15 Sep 2023 20:59:12 GMT
• Title: Biased Attention: Do Vision Transformers Amplify Gender Bias More than Convolutional Neural Networks?
• Authors: Abhishek Mandal, Susan Leavy, and Suzanne Little
• Abstract summary: Deep neural networks used in computer vision have been shown to exhibit many social biases such as gender bias. Vision Transformers (ViTs) have become increasingly popular in computer vision applications, outperforming Convolutional Neural Networks (CNNs) in many tasks such as image classification. This research found that ViTs amplified gender bias to a greater extent than CNNs.
• Score: 2.8391805742728553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural networks used in computer vision have been shown to exhibit many social biases such as gender bias. Vision Transformers (ViTs) have become increasingly popular in computer vision applications, outperforming Convolutional Neural Networks (CNNs) in many tasks such as image classification. However, given that research on mitigating bias in computer vision has primarily focused on CNNs, it is important to evaluate the effect of a different network architecture on the potential for bias amplification. In this paper we therefore introduce a novel metric to measure bias in architectures, Accuracy Difference. We examine bias amplification when models belonging to these two architectures are used as a part of large multimodal models, evaluating the different image encoders of Contrastive Language Image Pretraining which is an important model used in many generative models such as DALL-E and Stable Diffusion. Our experiments demonstrate that architecture can play a role in amplifying social biases due to the different techniques employed by the models for feature extraction and embedding as well as their different learning properties. This research found that ViTs amplified gender bias to a greater extent than CNNs

Related papers

• ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain [0.0]
  Vision Transformers (ViTs) are becoming more popular and dominant solutions for many vision problems. ViTs can overcome several possible difficulties with convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2023-10-09T12:31:30Z)
• A Multidimensional Analysis of Social Biases in Vision Transformers [15.98510071115958]
  We measure the impact of training data, model architecture, and training objectives on social biases in Vision Transformers (ViTs) Our findings indicate that counterfactual augmentation training using diffusion-based image editing can mitigate biases, but does not eliminate them. We find that larger models are less biased than smaller models, and that models trained using discriminative objectives are less biased than those trained using generative objectives.
  arXiv  Detail & Related papers  (2023-08-03T09:03:40Z)
• Feature-Learning Networks Are Consistent Across Widths At Realistic Scales [72.27228085606147]
  We study the effect of width on the dynamics of feature-learning neural networks across a variety of architectures and datasets. Early in training, wide neural networks trained on online data have not only identical loss curves but also agree in their point-wise test predictions throughout training. We observe, however, that ensembles of narrower networks perform worse than a single wide network.
  arXiv  Detail & Related papers  (2023-05-28T17:09:32Z)
• Connecting metrics for shape-texture knowledge in computer vision [1.7785095623975342]
  Deep neural networks remain brittle and susceptible to many changes in the image that do not cause humans to misclassify images. Part of this different behavior may be explained by the type of features humans and deep neural networks use in vision tasks.
  arXiv  Detail & Related papers  (2023-01-25T14:37:42Z)
• Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks [61.60177890353585]
  Deep convolutional neural networks (CNNs) have been shown to provide excellent models for its functional analogue in the brain, the ventral stream in visual cortex. Here we consider some prominent statistical patterns that are known to exist in the internal representations of either CNNs or the visual cortex. We show that CNNs and visual cortex share a similarly tight relationship between dimensionality expansion/reduction of object representations and reformatting of image information.
  arXiv  Detail & Related papers  (2022-05-27T08:06:40Z)
• Do Vision Transformers See Like Convolutional Neural Networks? [45.69780772718875]
  Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or even superior performance on image classification tasks. Are they acting like convolutional networks, or learning entirely different visual representations? We find striking differences between the two architectures, such as ViT having more uniform representations across all layers.
  arXiv  Detail & Related papers  (2021-08-19T17:27:03Z)
• Are Convolutional Neural Networks or Transformers more like human vision? [9.83454308668432]
  We show that attention-based networks can achieve higher accuracy than CNNs on vision tasks. These results have implications both for building more human-like vision models, as well as for understanding visual object recognition in humans.
  arXiv  Detail & Related papers  (2021-05-15T10:33:35Z)
• Transformers in Vision: A Survey [101.07348618962111]
  Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence. Transformers require minimal inductive biases for their design and are naturally suited as set-functions. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline.
  arXiv  Detail & Related papers  (2021-01-04T18:57:24Z)
• A Survey on Visual Transformer [126.56860258176324]
  Transformer is a type of deep neural network mainly based on the self-attention mechanism. In this paper, we review these vision transformer models by categorizing them in different tasks and analyzing their advantages and disadvantages.
  arXiv  Detail & Related papers  (2020-12-23T09:37:54Z)
• Learning from Failure: Training Debiased Classifier from Biased Classifier [76.52804102765931]
  We show that neural networks learn to rely on spurious correlation only when it is "easier" to learn than the desired knowledge. We propose a failure-based debiasing scheme by training a pair of neural networks simultaneously. Our method significantly improves the training of the network against various types of biases in both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2020-07-06T07:20:29Z)

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.