SpurAudio benchmark shows state-of-the-art few-shot audio classifiers suffer large performance drops when background correlations are disrupted, even in large pretrained models.
Meta-Learning with Latent Embedding Optimization
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.
representative citing papers
Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.
TAM aligns query video frames to novel class examples, averages per-frame distances along the path, and uses continuous relaxation for end-to-end few-shot optimization, yielding gains on Kinetics and Something-Something-V2.
MARCO achieves new state-of-the-art semantic correspondence on SPair-71k, AP-10K and PF-PASCAL by combining coarse-to-fine refinement with self-distillation on DINOv2, delivering larger gains at fine thresholds and on unseen keypoints and categories while using 3x fewer parameters and running 10x更快.
Frozen DINOv2-L features with k-NN classification and PCA/ICA refinement achieve state-of-the-art few-shot performance on four benchmarks without any backpropagation or fine-tuning.
citing papers explorer
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Rethinking the Good Enough Embedding for Easy Few-Shot Learning
Frozen DINOv2-L features with k-NN classification and PCA/ICA refinement achieve state-of-the-art few-shot performance on four benchmarks without any backpropagation or fine-tuning.