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Found 11 papers, 5 papers with code
Controlled Training Data Generation with Diffusion Models
In this work, we present a method to control a text-to-image generative model to produce training data specifically "useful" for supervised learning.
Unraveling the Key Components of OOD Generalization via Diversification
We show that (1) diversification methods are highly sensitive to the distribution of the unlabeled data used for diversification and can underperform significantly when away from a method-specific sweet spot.
Task Discovery: Finding the Tasks that Neural Networks Generalize on
An intriguing question would be: what if, instead of fixing the task and searching in the model space, we fix the model and search in the task space?
MultiMAE: Multi-modal Multi-task Masked Autoencoders
We show this pre-training strategy leads to a flexible, simple, and efficient framework with improved transfer results to downstream tasks.
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Semantic Segmentation
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3D Common Corruptions and Data Augmentation
We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural networks.
Simple Control Baselines for Evaluating Transfer Learning
Transfer learning has witnessed remarkable progress in recent years, for example, with the introduction of augmentation-based contrastive self-supervised learning methods.
Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves
Self-supervised learning has witnessed remarkable progress in recent years, in particular with the introduction of augmentation-based contrastive methods.
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations
Averaging predictions over a set of models -- an ensemble -- is widely used to improve predictive performance and uncertainty estimation of deep learning models.
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning.
The Deep Weight Prior
Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution.
Uncertainty Estimation via Stochastic Batch Normalization
In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation.
