Classification with Binary Neural Network
7 papers with code • 3 benchmarks • 3 datasets
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Most implemented papers
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
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
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
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
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
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
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).