Dataset distillation on MNIST and CIFAR10
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60K images 10 images distill 50K images 94% accuracy 54% accuracy train 100 images Fixed init 100 images encoding domain difference 85% MNIST accuracy 300 attack images distill train train train Fixed init 52% MNIST accuracy Trained for SVHN distill 60K images 50K images distill Trained for CIFAR10 82% accuracy on class “plane” Attacked Model 7% accuracy on class “plane” 73K images Dataset distillation on MNIST and CIFAR10 Dataset distillation can quickly fine-tune pre-trained networks on new datasets Dataset distillation can maliciously attack classifier networks 13% accuracy 9% accuracy
Transcript of Dataset distillation on MNIST and CIFAR10
60K images 10 images
distill
50K images
94% accuracy
54% accuracy
train
100 images
Fixed init
100 imagesencoding domain difference
85% MNISTaccuracy
300 attack images
distill train
train
train
Fixed init
52% MNIST accuracy
Trained for SVHN
distill
60K images
50K images
distill Trained for CIFAR10
82% accuracyon class “plane”
Attacked Model
7% accuracyon class “plane”
73K images
Dataset distillation on MNIST and CIFAR10
Dataset distillation can quickly fine-tune pre-trained networks on new datasets
Dataset distillation can maliciously attack classifier networks
13% accuracy
9% accuracy