Classification with Binary Neural Network
7 papers with code • 3 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Classification with Binary Neural Network
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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.
AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets
huawei-noah/Efficient-Computing
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Since the modern deep neural networks are of sophisticated design with complex architecture for the accuracy reason, the diversity on distributions of weights and activations is very high.
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.
High-Capacity Expert Binary Networks
1adrianb/expert-binary-networks
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ICLR 2021
Network binarization is a promising hardware-aware direction for creating efficient deep models.
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).
