In practice, when training denoisers, we sample multiple noisy samples for each clean image and minimize the Mean Squared Error (MSE) loss for recovering the clean image.

In practice, when training denoisers, we sample multiple noisy samples for each clean image and minimize the Mean Squared Error (MSE) loss for recovering the clean image.

Visual and auditory perception is crucial for observing the world. When we hear a sound, our brain will extract semantic information and locate the sounding source.

In practice, however, such an assumption is extremely expensive even impossible to satisfy. Based on this observation, we reveal and study alatent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisylabels.

Our approach reduces noise levelbysimply constructing meta-classes and improves accuracyviacombinatorial inferences overmultipleconstituent classifiers.