# 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## 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.

# 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.

# 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).