Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition
This work addresses calibration issues for models in long-tailed visual recognition, which is important for applications like autonomous driving and medical imaging, but it is incremental as it builds on existing temperature scaling and label smoothing techniques.
The paper tackles the problem of miscalibration in deep neural networks trained on long-tailed datasets, where models are overly confident for high-frequency classes, by proposing class-distribution-aware temperature scaling and label smoothing methods that incorporate class frequency information, resulting in improved calibration error and predictive accuracy.
Despite impressive accuracy, deep neural networks are often miscalibrated and tend to overly confident predictions. Recent techniques like temperature scaling (TS) and label smoothing (LS) show effectiveness in obtaining a well-calibrated model by smoothing logits and hard labels with scalar factors, respectively. However, the use of uniform TS or LS factor may not be optimal for calibrating models trained on a long-tailed dataset where the model produces overly confident probabilities for high-frequency classes. In this study, we propose class-distribution-aware TS (CDA-TS) and LS (CDA-LS) by incorporating class frequency information in model calibration in the context of long-tailed distribution. In CDA-TS, the scalar temperature value is replaced with the CDA temperature vector encoded with class frequency to compensate for the over-confidence. Similarly, CDA-LS uses a vector smoothing factor and flattens the hard labels according to their corresponding class distribution. We also integrate CDA optimal temperature vector with distillation loss, which reduces miscalibration in self-distillation (SD). We empirically show that class-distribution-aware TS and LS can accommodate the imbalanced data distribution yielding superior performance in both calibration error and predictive accuracy. We also observe that SD with an extremely imbalanced dataset is less effective in terms of calibration performance. Code is available in https://github.com/mobarakol/Class-Distribution-Aware-TS-LS.