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Found 8 papers, 2 papers with code
Lipsum-FT: Robust Fine-Tuning of Zero-Shot Models Using Random Text Guidance
Large-scale contrastive vision-language pre-trained models provide the zero-shot model achieving competitive performance across a range of image classification tasks without requiring training on downstream data.
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling
The nonparametric learning (NPL) method is a recent approach that employs a nonparametric prior for posterior sampling, efficiently accounting for model misspecification scenarios, which is suitable for transfer learning scenarios that may involve the distribution shift between upstream and downstream tasks.
Traversing Between Modes in Function Space for Fast Ensembling
While this provides a way to efficiently train ensembles, for inference, multiple forward passes should still be executed using all the ensemble parameters, which often becomes a serious bottleneck for real-world deployment.
Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
Given the ever-increasing size of modern neural networks, the significance of sparse architectures has surged due to their accelerated inference speeds and minimal memory demands.
Martingale Posterior Neural Processes
Based on this result, instead of assuming any form of the latent variables, we equip a NP with a predictive distribution implicitly defined with neural networks and use the corresponding martingale posteriors as the source of uncertainty.
Decoupled Training for Long-Tailed Classification With Stochastic Representations
Decoupling representation learning and classifier learning has been shown to be effective in classification with long-tailed data.
Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation
Ensembles of deep neural networks have demonstrated superior performance, but their heavy computational cost hinders applying them for resource-limited environments.
Diversity Matters When Learning From Ensembles
We propose a simple approach for reducing this gap, i. e., making the distilled performance close to the full ensemble.
