mlsanityData to 7 Oct 2026

HyperShot: Few-Shot Learning by Kernel HyperNetworks

arXiv 2203.11378 ↗First posted 21 Mar 2022cs.LG cs.AI cs.CVcited by 24

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Abstract (arXiv)

Few-shot models aim at making predictions using a minimal number of labeled examples from a given task. The main challenge in this area is the one-shot setting where only one element represents each class. We propose HyperShot - the fusion of kernels and hypernetwork paradigm. Compared to reference approaches that apply a gradient-based adjustment of the parameters, our model aims to switch the classification module parameters depending on the task's embedding. In practice, we utilize a hypernetwork, which takes the aggregated information from support data and returns the classifier's parameters handcrafted for the considered problem. Moreover, we introduce the kernel-based representation of the support examples delivered to hypernetwork to create the parameters of the classification module. Consequently, we rely on relations between embeddings of the support examples instead of direct feature values provided by the backbone models. Thanks to this approach, our model can adapt to highly different tasks.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 53 of its 67 references that are in the map. First posted on arXiv 21 Mar 2022.

Builds on

53 of 67 references.

References · 53

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38 more
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Citing papers per year: 2022: 4; 2023: 8; 2024: 5; 2025: 6; 2026: 1.

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  1. 2023A Brief Review of Hypernetworks in Deep Learningcited by 102
  2. 2025Model Predictive Task Sampling for Efficient and Robust Adaptationcited by 20
  3. 2022HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworkscited by 16
  4. 2023HyP-NeRF: Learning Improved NeRF Priors using a HyperNetworkcited by 14
  5. 2022HMOE: Hypernetwork-based Mixture of Experts for Domain Generalizationcited by 8
  6. 2022INR-V: A Continuous Representation Space for Video-based Generative Taskscited by 7
  7. 2023HyperNeRFGAN: Hypernetwork approach to 3D NeRF GANcited by 7
  8. 2024A Survey of Few-Shot Learning for Biomedical Time Seriescited by 7
  9. 2025Hypencoder: Hypernetworks for Information Retrievalcited by 7
  10. 2023Task-Oriented Channel Attention for Fine-Grained Few-Shot Classificationcited by 4
  11. 2023HyperMask: Adaptive Hypernetwork-based Masks for Continual Learningcited by 4
  12. 2024Improving Fairness and Mitigating MADness in Generative Modelscited by 4
12 more
  1. 2024Neural Metamorphosiscited by 4
  2. 2023HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settingscited by 2
  3. 2023Domain Generalization Deep Graph Transformationcited by 1
  4. 2024HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptationcited by 1
  5. 2025Exploiting Task Relationships in Continual Learning via Transferability-Aware Task Embeddingscited by 1
  6. 2025Hypernetworks for Model-Heterogeneous Personalized Federated Learningcited by 1
  7. 2022Hypernetwork approach to Bayesian MAMLcited by 0
  8. 2023Language-Guided Reinforcement Learning for Hard Attention in Few-Shot Learningcited by 0
  9. 2024D'OH: Decoder-Only Random Hypernetworks for Implicit Neural Representationscited by 0
  10. 2025HyConEx: Hypernetwork classifier with counterfactual explanations for tabular datacited by 0
  11. 2025Keyword Spotting with Hyper-Matched Filters for Small Footprint Devicescited by 0
  12. 2026PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decodingcited by 0