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Found 6 papers, 3 papers with code
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach
To incorporate sufficient diversity into the IoT training data, one therefore needs to consider a combinatorial explosion of training cases that are multiplicative in the number of objects considered and the possible environmental conditions in which such objects may be encountered.
Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learners
Existing approaches generally assume that the shot number at test time is known in advance.
Few-Shot Classification with Feature Map Reconstruction Networks
In this paper we reformulate few-shot classification as a reconstruction problem in latent space.
Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation
We aim to build image generation models that generalize to new domains from few examples.
Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition
Few-shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes (e. g., birds) based on a few images alone.
Few-Shot Learning with Localization in Realistic Settings
Traditional recognition methods typically require large, artificially-balanced training classes, while few-shot learning methods are tested on artificially small ones.
