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Found 10 papers, 7 papers with code
Does Progress On Object Recognition Benchmarks Improve Real-World Generalization?
Second, we study model generalization across geographies by measuring the disparities in performance across regions, a more fine-grained measure of real world generalization.
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers
Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure.
On Feature Learning in the Presence of Spurious Correlations
Deep classifiers are known to rely on spurious features $\unicode{x2013}$ patterns which are correlated with the target on the training data but not inherently relevant to the learning problem, such as the image backgrounds when classifying the foregrounds.
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
Neural network classifiers can largely rely on simple spurious features, such as backgrounds, to make predictions.
Ranked #1 on
Out-of-Distribution Generalization
on UrbanCars
Task-agnostic Continual Learning with Hybrid Probabilistic Models
Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learning.
Does Knowledge Distillation Really Work?
Knowledge distillation is a popular technique for training a small student network to emulate a larger teacher model, such as an ensemble of networks.
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Detecting out-of-distribution (OOD) data is crucial for robust machine learning systems.
Semi-Supervised Learning with Normalizing Flows
Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood.
Semi-Supervised Image Classification
Semi-Supervised Text Classification
Subspace Inference for Bayesian Deep Learning
Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty.
SWALP : Stochastic Weight Averaging in Low-Precision Training
Low precision operations can provide scalability, memory savings, portability, and energy efficiency.
