A Fair Loss Function for Network Pruning

• URL: http://arxiv.org/abs/2211.10285v2
• Date: Mon, 18 Nov 2024 02:50:46 GMT
• Title: A Fair Loss Function for Network Pruning
• Authors: Robbie Meyer, Alexander Wong,
• Abstract summary: We introduce the performance weighted loss function, a simple modified cross-entropy loss function that can be used to limit the introduction of biases during pruning. Experiments using the CelebA, Fitzpatrick17k and CIFAR-10 datasets demonstrate that the proposed method is a simple and effective tool.
• Score: 70.35230425589592
• License:
• Abstract: Model pruning can enable the deployment of neural networks in environments with resource constraints. While pruning may have a small effect on the overall performance of the model, it can exacerbate existing biases into the model such that subsets of samples see significantly degraded performance. In this paper, we introduce the performance weighted loss function, a simple modified cross-entropy loss function that can be used to limit the introduction of biases during pruning. Experiments using the CelebA, Fitzpatrick17k and CIFAR-10 datasets demonstrate that the proposed method is a simple and effective tool that can enable existing pruning methods to be used in fairness sensitive contexts. Code used to produce all experiments contained in this paper can be found at https://github.com/robbiemeyer/pw_loss_pruning.

Related papers

• FGP: Feature-Gradient-Prune for Efficient Convolutional Layer Pruning [16.91552023598741]
  This paper introduces a novel pruning method called Feature-Gradient Pruning (FGP) It integrates both feature-based and gradient-based information to more effectively evaluate the importance of channels across various target classes. Experiments conducted across multiple tasks and datasets show that FGP significantly reduces computational costs and minimizes accuracy loss.
  arXiv  Detail & Related papers  (2024-11-19T08:42:15Z)
• SINDER: Repairing the Singular Defects of DINOv2 [61.98878352956125]
  Vision Transformer models trained on large-scale datasets often exhibit artifacts in the patch token they extract. We propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset.
  arXiv  Detail & Related papers  (2024-07-23T20:34:23Z)
• Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression [12.44857030152608]
  Deep Neural Networks are prone to learning and relying on spurious correlations in the training data, which, for high-risk applications, can have fatal consequences. Various approaches to suppress model reliance on harmful features have been proposed that can be applied post-hoc without additional training. We propose a reactive approach conditioned on model-derived knowledge and eXplainable Artificial Intelligence (XAI) insights.
  arXiv  Detail & Related papers  (2024-04-15T09:16:49Z)
• Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting [62.23057729112182]
  Differentiable score-based causal discovery methods learn a directed acyclic graph from observational data. We propose a model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore.
  arXiv  Detail & Related papers  (2023-03-06T14:49:59Z)
• Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps [85.49020931411825]
  Convolutional Neural Networks (CNNs) compression is crucial to deploying these models in edge devices with limited resources. We propose to address the channel pruning problem from a novel perspective by leveraging the interpretations of a model to steer the pruning process. We tackle this challenge by introducing a selector model that predicts real-time smooth saliency masks for pruned models.
  arXiv  Detail & Related papers  (2022-09-07T01:12:11Z)
• Fine-grained Retrieval Prompt Tuning [149.9071858259279]
  Fine-grained Retrieval Prompt Tuning steers a frozen pre-trained model to perform the fine-grained retrieval task from the perspectives of sample prompt and feature adaptation. Our FRPT with fewer learnable parameters achieves the state-of-the-art performance on three widely-used fine-grained datasets.
  arXiv  Detail & Related papers  (2022-07-29T04:10:04Z)
• Role of Orthogonality Constraints in Improving Properties of Deep Networks for Image Classification [8.756814963313804]
  We propose an Orthogonal Sphere (OS) regularizer that emerges from physics-based latent-representations under simplifying assumptions. Under further simplifying assumptions, the OS constraint can be written in closed-form as a simple orthonormality term and be used along with the cross-entropy loss function. We demonstrate the effectiveness of the proposed OS regularization by providing quantitative and qualitative results on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-22T18:46:05Z)
• Salvage Reusable Samples from Noisy Data for Robust Learning [70.48919625304]
  We propose a reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks.
  arXiv  Detail & Related papers  (2020-08-06T02:07:21Z)

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.