Title: TrainRef: Curating Data with Label Distribution and Minimal Reference for Accurate Prediction and Reliable Confidence
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Label misinformation, data curation, influence function

Abstract: Practical classification requires both high predictive accuracy and reliable confidence for human-AI collaboration. Given that a high-quality dataset is expensive and sometimes impossible, learning with noisy labels (LNL) is of great importance. The state-of-the-art works propose many denoising approaches by categorically correcting the label noise, i.e., change a label from one class to another. While effective in improving accuracy, they are less effective for learning reliable confidence. This happens especially when the number of classes grows, giving rise to more ambiguous samples. In addition, traditional approaches usually curate the training dataset (e.g., reweighting samples or correcting data labels) by intrinsically learning normalities from the noisy dataset. The curation performance can suffer when the noisy ratio is high enough to form a polluting normality.

In this work, we propose a training-time data-curation framework, TrainRef, to uniformly address predictive accuracy and confidence calibration by (1) an extrinsic small set of reference samples $D_{{ref}}$ to avoid normality pollution and (2) curate labels into a class distribution instead of a categorical class to handle sample ambiguity. Our insights lie in that the extrinsic information allows us to select more precise clean samples even when $|D_{{ref}}|$ equals to the number of classes (i.e., one sample per class). Technically, we design (1) a reference augmentation technique to select clean samples from the dataset based on $D_{{ref}}$; and (2) a model-dataset co-evolving technique for a near-perfect embedding space, which is used to vote on the class-distribution for the label of a noisy sample. Extensive experiments on CIFAR-100, Animal10N, and WebVision demonstrate that TrainRef outperform the state-of-the-art denoising techniques (DISC, L2B, and DivideMix) and model calibration techniques (label smoothing, Mixup, and temperature scaling). Furthermore, our user study shows that the model confidence trained by TrainRef well aligns with human intuition. More demonstration, proof, and experimental details are available at https://sites.google.com/view/train-ref.