A Biologically Inspired Feature Enhancement Framework for Zero-Shot Learning

• URL: http://arxiv.org/abs/2005.08704v1
• Date: Wed, 13 May 2020 13:25:22 GMT
• Title: A Biologically Inspired Feature Enhancement Framework for Zero-Shot Learning
• Authors: Zhongwu Xie, Weipeng Cao, Xizhao Wang, Zhong Ming, Jingjing Zhang, Jiyong Zhang
• Abstract summary: This paper proposes a biologically inspired feature enhancement framework for Zero-Shot Learning (ZSL) algorithms. Specifically, we design a dual-channel learning framework that uses auxiliary data sets to enhance the feature extractor of the ZSL model. Our proposed method can effectively improve the ability of the ZSL model and achieve state-of-the-art results on three benchmark ZSL tasks.
• Score: 18.348568695197553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most of the Zero-Shot Learning (ZSL) algorithms currently use pre-trained models as their feature extractors, which are usually trained on the ImageNet data set by using deep neural networks. The richness of the feature information embedded in the pre-trained models can help the ZSL model extract more useful features from its limited training samples. However, sometimes the difference between the training data set of the current ZSL task and the ImageNet data set is too large, which may lead to the use of pre-trained models has no obvious help or even negative impact on the performance of the ZSL model. To solve this problem, this paper proposes a biologically inspired feature enhancement framework for ZSL. Specifically, we design a dual-channel learning framework that uses auxiliary data sets to enhance the feature extractor of the ZSL model and propose a novel method to guide the selection of the auxiliary data sets based on the knowledge of biological taxonomy. Extensive experimental results show that our proposed method can effectively improve the generalization ability of the ZSL model and achieve state-of-the-art results on three benchmark ZSL tasks. We also explained the experimental phenomena through the way of feature visualization.

Related papers

• Can Generative Models Improve Self-Supervised Representation Learning? [0.7999703756441756]
  We introduce a novel framework that enriches the self-supervised learning paradigm by utilizing generative models to produce semantically consistent image augmentations. Our results show that our framework significantly enhances the quality of learned visual representations by up to 10% Top-1 accuracy in downstream tasks.
  arXiv  Detail & Related papers  (2024-03-09T17:17:07Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Understanding and Improving the Role of Projection Head in Self-Supervised Learning [77.59320917894043]
  Self-supervised learning (SSL) aims to produce useful feature representations without access to human-labeled data annotations. Current contrastive learning approaches append a parametrized projection head to the end of some backbone network to optimize the InfoNCE objective. This raises a fundamental question: Why is a learnable projection head required if we are to discard it after training?
  arXiv  Detail & Related papers  (2022-12-22T05:42:54Z)
• On the Transferability of Visual Features in Generalized Zero-Shot Learning [28.120004119724577]
  Generalized Zero-Shot Learning (GZSL) aims to train a classifier that can generalize to unseen classes. In this work, we investigate the utility of different GZSL methods when using different feature extractors. We also examine how these models' pre-training objectives, datasets, and architecture design affect their feature representation ability.
  arXiv  Detail & Related papers  (2022-11-22T18:59:09Z)
• Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning [37.00463358780726]
  We propose a Neural Network model that learns a latent feature embedding and a Gaussian Process (GP) regression model that predicts latent feature prototypes of unseen classes. Our model is trained efficiently with a simple training strategy that mitigates the impact of class-imbalanced training data.
  arXiv  Detail & Related papers  (2022-10-11T04:57:20Z)
• Improving Self-Supervised Learning by Characterizing Idealized Representations [155.1457170539049]
  We prove necessary and sufficient conditions for any task invariant to given data augmentations. For contrastive learning, our framework prescribes simple but significant improvements to previous methods. For non-contrastive learning, we use our framework to derive a simple and novel objective.
  arXiv  Detail & Related papers  (2022-09-13T18:01:03Z)
• Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding [62.17020485045456]
  It is commonly assumed in semi-supervised learning (SSL) that the unlabeled data are drawn from the same distribution as that of the labeled ones. We propose to selectively utilize unlabeled data through sample weighting, so that only conducive unlabeled data would be prioritized.
  arXiv  Detail & Related papers  (2022-05-02T16:09:17Z)
• Multi-Head Self-Attention via Vision Transformer for Zero-Shot Learning [11.66422653137002]
  We propose an attention-based model in the problem settings of Zero-Shot Learning to learn attributes useful for unseen class recognition. Our method uses an attention mechanism adapted from Vision Transformer to capture and learn discriminative attributes by splitting images into small patches.
  arXiv  Detail & Related papers  (2021-07-30T19:08:44Z)
• FREE: Feature Refinement for Generalized Zero-Shot Learning [86.41074134041394]
  Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts dedicated to overcoming the problems of visual-semantic domain gap and seen-unseen bias. Most existing methods directly use feature extraction models trained on ImageNet alone, ignoring the cross-dataset bias between ImageNet and GZSL benchmarks. We propose a simple yet effective GZSL method, termed feature refinement for generalized zero-shot learning (FREE) to tackle the above problem.
  arXiv  Detail & Related papers  (2021-07-29T08:11:01Z)
• On Data-Augmentation and Consistency-Based Semi-Supervised Learning [77.57285768500225]
  Recently proposed consistency-based Semi-Supervised Learning (SSL) methods have advanced the state of the art in several SSL tasks. Despite these advances, the understanding of these methods is still relatively limited.
  arXiv  Detail & Related papers  (2021-01-18T10:12:31Z)

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.