Classification with Binary Neural Network
5 papers with code • 3 benchmarks • 3 datasets
Libraries
Use these libraries to find Classification with Binary Neural Network models and implementations
CIFAR-10
ImageNet
CIFAR-100
Most implemented papers
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
hpi-xnor/BMXNet
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We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks.
Improved training of binary networks for human pose estimation and image recognition
1adrianb/binary-networks-pytorch
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Big neural networks trained on large datasets have advanced the state-of-the-art for a large variety of challenging problems, improving performance by a large margin.
XNOR-Net++: Improved Binary Neural Networks
1adrianb/binary-networks-pytorch
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This paper proposes an improved training algorithm for binary neural networks in which both weights and activations are binary numbers.
Training Binary Neural Networks with Real-to-Binary Convolutions
This paper shows how to train binary networks to within a few percent points ($\sim 3-5 \%$) of the full precision counterpart.
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).
