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Found 10 papers, 3 papers with code
NICO++: Towards Better Benchmarking for Domain Generalization
Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains.
Towards Domain Generalization in Object Detection
Despite the striking performance achieved by modern detectors when training and test data are sampled from the same or similar distribution, the generalization ability of detectors under unknown distribution shifts remains hardly studied.
Regulatory Instruments for Fair Personalized Pricing
Personalized pricing is a business strategy to charge different prices to individual consumers based on their characteristics and behaviors.
Why Stable Learning Works? A Theory of Covariate Shift Generalization
We first specify a set of variables, named minimal stable variable set, that is minimal and optimal to deal with covariate shift generalization for common loss functions, including the mean squared loss and binary cross entropy loss.
Towards Out-Of-Distribution Generalization: A Survey
Classic machine learning methods are built on the $i. i. d.$ assumption that training and testing data are independent and identically distributed.
Towards Unsupervised Domain Generalization
Domain generalization (DG) aims to help models trained on a set of source domains generalize better on unseen target domains.
Deep Stable Learning for Out-Of-Distribution Generalization
Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise.
Ranked #12 on
Domain Generalization
on VLCS
Sample Balancing for Improving Generalization under Distribution Shifts
We propose to address this problem by removing the dependencies between features via reweighting training samples, which results in a more balanced distribution and helps deep models get rid of spurious correlations and, in turn, concentrate more on the true connection between features and labels.
Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation
To alleviate the problem, we propose to classify input features into intermediate shape codes and recover complete object shapes from them.
Spatio-Temporal Fusion Based Convolutional Sequence Learning for Lip Reading
Current state-of-the-art approaches for lip reading are based on sequence-to-sequence architectures that are designed for natural machine translation and audio speech recognition.
Ranked #9 on
Lipreading
on LRS2
(using extra training data)
