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Found 8 papers, 5 papers with code
Do We Need Zero Training Loss After Achieving Zero Training Error?
We experimentally show that flooding improves performance and, as a byproduct, induces a double descent curve of the test loss.
Binary Classification from Positive-Confidence Data
Can we learn a binary classifier from only positive data, without any negative data or unlabeled data?
Learning from Complementary Labels
Collecting complementary labels would be less laborious than collecting ordinary labels, since users do not have to carefully choose the correct class from a long list of candidate classes.
Complementary-Label Learning for Arbitrary Losses and Models
In contrast to the standard classification paradigm where the true class is given to each training pattern, complementary-label learning only uses training patterns each equipped with a complementary label, which only specifies one of the classes that the pattern does not belong to.
Ranked #22 on
Image Classification
on Kuzushiji-MNIST
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification
In contrast to others, our method is model-free and even instance-free.
LocalDrop: A Hybrid Regularization for Deep Neural Networks
In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas.
Flooding Regularization for Stable Training of Generative Adversarial Networks
One of the main approaches to address this problem is to modify the loss function, often using regularization terms in addition to changing the type of adversarial losses.
The Selected-completely-at-random Complementary Label is a Practical Weak Supervision for Multi-class Classification
Complementary-label learning is a weakly supervised learning problem in which each training example is associated with one or multiple complementary labels indicating the classes to which it does not belong.
