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Zero-Shot Learning Based on Knowledge Sharing

arXiv 2102.13326 ↗First posted 26 Feb 2021cs.CV cs.AIcited by 0

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Abstract (arXiv)

Zero-Shot Learning (ZSL) is an emerging research that aims to solve the classification problems with very few training data. The present works on ZSL mainly focus on the mapping of learning semantic space to visual space. It encounters many challenges that obstruct the progress of ZSL research. First, the representation of the semantic feature is inadequate to represent all features of the categories. Second, the domain drift problem still exists during the transfer from semantic space to visual space. In this paper, we introduce knowledge sharing (KS) to enrich the representation of semantic features. Based on KS, we apply a generative adversarial network to generate pseudo visual features from semantic features that are very close to the real visual features. Abundant experimental results from two benchmark datasets of ZSL show that the proposed approach has a consistent improvement.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 26 Feb 2021.

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20 of 34 references.

References · 20

  1. 2017Improved Training of Wasserstein GANscited by 2,223
  2. 2016Least Squares Generative Adversarial Networkscited by 1,000
  3. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  4. 2017Zero-Shot Learning -- The Good, the Bad and the Uglycited by 254
  5. 2014Evaluation of Output Embeddings for Fine-Grained Image Classificationcited by 166
  6. 2016Learning a Deep Embedding Model for Zero-Shot Learningcited by 153
  7. 2017Semantic Autoencoder for Zero-Shot Learningcited by 133
  8. 2017BEGAN: Boundary Equilibrium Generative Adversarial Networkscited by 121
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  11. 2015Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptionscited by 64
  12. 2015Transductive Multi-view Zero-Shot Learningcited by 53
8 more
  1. 2017Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precisioncited by 46
  2. 2015Ridge Regression, Hubness, and Zero-Shot Learningcited by 44
  3. 2016Less is more: zero-shot learning from online textual documents with noise suppressioncited by 40
  4. 2014Zero Shot Recognition with Unreliable Attributescited by 39
  5. 2014A Unified Perspective on Multi-Domain and Multi-Task Learningcited by 33
  6. 2016Multi-Cue Zero-Shot Learning with Strong Supervisioncited by 22
  7. 2015Write a Classifier: Predicting Visual Classifiers from Unstructured Textcited by 9
  8. 2019Unified Generator-Classifier for Efficient Zero-Shot Learningcited by 2

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