  We present FerKD, a novel efficient knowledge distillation framework that
incorporates partial soft-hard label adaptation coupled with a
region-calibration mechanism. Our approach stems from the observation and
intuition that standard data augmentations, such as RandomResizedCrop, tend to
transform inputs into diverse conditions: easy positives, hard positives, or
hard negatives. In traditional distillation frameworks, these transformed
samples are utilized equally through their predictive probabilities derived
from pretrained teacher models. However, merely relying on prediction values
from a pretrained teacher, a common practice in prior studies, neglects the
reliability of these soft label predictions. To address this, we propose a new
scheme that calibrates the less-confident regions to be the context using
softened hard groundtruth labels. Our approach involves the processes of hard
regions mining + calibration. We demonstrate empirically that this method can
dramatically improve the convergence speed and final accuracy. Additionally, we
find that a consistent mixing strategy can stabilize the distributions of soft
supervision, taking advantage of the soft labels. As a result, we introduce a
stabilized SelfMix augmentation that weakens the variation of the mixed images
and corresponding soft labels through mixing similar regions within the same
image. FerKD is an intuitive and well-designed learning system that eliminates
several heuristics and hyperparameters in former FKD solution. More
importantly, it achieves remarkable improvement on ImageNet-1K and downstream
tasks. For instance, FerKD achieves 81.2% on ImageNet-1K with ResNet-50,
outperforming FKD and FunMatch by remarkable margins. Leveraging better
pre-trained weights and larger architectures, our finetuned ViT-G14 even
achieves 89.9%. Our code is available at
https://github.com/szq0214/FKD/tree/main/FerKD.
