A self-distillation framework that aligns features of original and gradient-perturbed inputs reports an accuracy jump from 22.93% to 41.99% on CIFAR-100 and improved SSIM/FID on CUB.
Deep residual learning for image recognition
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Self Distillation via Iterative Constructive Perturbations
A self-distillation framework that aligns features of original and gradient-perturbed inputs reports an accuracy jump from 22.93% to 41.99% on CIFAR-100 and improved SSIM/FID on CUB.