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Found 5 papers, 5 papers with code
MAGMA: Manifold Regularization for MAEs
Masked Autoencoders (MAEs) are an important divide in self-supervised learning (SSL) due to their independence from augmentation techniques for generating positive (and/or negative) pairs as in contrastive frameworks.
GeNIe: Generative Hard Negative Images Through Diffusion
To achieve this, we adjust the noise level (equivalently, number of diffusion iterations) to ensure the generated image retains low-level and background features from the source image while representing the target category, resulting in a hard negative sample for the source category.
Self-Attention Message Passing for Contrastive Few-Shot Learning
Humans have a unique ability to learn new representations from just a handful of examples with little to no supervision.
Contrastive Learning
Unsupervised Few-Shot Image Classification
+1
Transductive Decoupled Variational Inference for Few-Shot Classification
The versatility to learn from a handful of samples is the hallmark of human intelligence.
Federated Learning with Taskonomy for Non-IID Data
Classical federated learning approaches incur significant performance degradation in the presence of non-IID client data.
