Gradient-based optimization learns symmetric Gaussian mixture modes for 2-bit fixed-point weight quantization, claiming state-of-the-art performance and self-adaptive weights.
MNIST handwritten digit database
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2019 3verdicts
UNVERDICTED 3representative citing papers
ADIS-GAN disentangles affine transformations in a GAN to achieve over 98% classification accuracy on MNIST within 30 degrees rotation and over 90% under FGSM and PGD attacks while generating rotation and scaling factors.
A generative-model-driven introspection method produces counterfactual image edits to explain deep neural network predictions on MNIST and CelebA.
citing papers explorer
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Learning Multimodal Fixed-Point Weights using Gradient Descent
Gradient-based optimization learns symmetric Gaussian mixture modes for 2-bit fixed-point weight quantization, claiming state-of-the-art performance and self-adaptive weights.
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Affine Disentangled GAN for Interpretable and Robust AV Perception
ADIS-GAN disentangles affine transformations in a GAN to achieve over 98% classification accuracy on MNIST within 30 degrees rotation and over 90% under FGSM and PGD attacks while generating rotation and scaling factors.
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Generative Counterfactual Introspection for Explainable Deep Learning
A generative-model-driven introspection method produces counterfactual image edits to explain deep neural network predictions on MNIST and CelebA.