Classification with Binary Neural Network

5 papers with code • 3 benchmarks • 3 datasets

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Use these libraries to find Classification with Binary Neural Network models and implementations

Most implemented papers

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

hpi-xnor/BMXNet 16 Mar 2016

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 11 Apr 2019

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 30 Sep 2019

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

brais-martinez/real2binary ICLR 2020

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 17 Mar 2021

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).