Classification with Binary Weight Network
3 papers with code • 3 benchmarks • 3 datasets
CIFAR-10
CIFAR-100
MNIST
Most implemented papers
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network
chrundle/biprop
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In this paper, we propose (and prove) a stronger Multi-Prize Lottery Ticket Hypothesis: A sufficiently over-parameterized neural network with random weights contains several subnetworks (winning tickets) that (a) have comparable accuracy to a dense target network with learned weights (prize 1), (b) do not require any further training to achieve prize 1 (prize 2), and (c) is robust to extreme forms of quantization (i. e., binary weights and/or activation) (prize 3).
Exact Backpropagation in Binary Weighted Networks with Group Weight Transformations
YanivShu/binary_weighted_networks_public
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Quantization based model compression serves as high performing and fast approach for inference that yields models which are highly compressed when compared to their full-precision floating point counterparts.
Information Theoretic Representation Distillation
roymiles/ITRD
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Despite the empirical success of knowledge distillation, current state-of-the-art methods are computationally expensive to train, which makes them difficult to adopt in practice.
