May 16, 2016 · Abstract:We present a memory and computation efficient ternary weight networks (TWNs) - with weights constrained to +1, 0 and -1.
We present a memory and computation efficient ternary weight networks (TWNs) - with weights constrained to +1, 0 and -1. The Euclidian distance between full ( ...
This is a Pytorch implementation of Ternary-Weights-Network for the MNIST dataset.The model structure is LeNet-5. The dataset is provided by torchvision.
We present a memory and computation efficient ternary weight networks (TWNs) - with weights constrained to +1, 0 and -1. The Euclidian distance between full ( ...
Extensive experiments show that the TWNs achieve much better result than the Binary-Weight-Networks (BWNs) and the classification performance on MNIST and ...
May 16, 2016 · We introduce Ternary Weight Networks (TWNs) - neural networks with weights constrained to +1, 0 and -1. The L2 distance between the full ...
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In this paper, we focus on training ternary weights without considering discrete activations. Ternary weight networks are different from binary weight networks ...
To solve this problem, we propose Trained. Ternary Quantization (TTQ), a method that can reduce the precision of weights in neural networks to ternary values.
Ternary Weight Networks (TWNs [33]) quantize the weights into {−α, 0,α} ... weights and ternary inputs, to teach the student networks which both weights.
A ternary weight network can be considered as a sparser version of the binary weight counterpart by replacing some -1s or 1s in the binary weights with 0s, thus ...