Papers Classification with Binary Weight Network
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Efficiera Residual Networks: Hardware-Friendly Fully Binary Weight with 2-bit Activation Model Achieves Practical ImageNet Accuracy
The edge-device environment imposes severe resource limitations, encompassing computation costs, hardware resource usage, and energy consumption for deploying deep neural network models. Ultra-low-bit quantization and ha…
BinarizationClassification with Binary Weight NetworkImage ClassificationQuantizationInformation Theoretic Representation Distillation
Despite the empirical success of knowledge distillation, current state-of-the-art methods are computationally expensive to train, which makes them difficult to adopt in practice. To address this problem, we introduce two…
Classification with Binary Weight NetworkKnowledge DistillationQuestion AnsweringExact Backpropagation in Binary Weighted Networks with Group Weight Transformations
Quantization based model compression serves as high performing and fast approach for inference that yields models which are highly compressed when compared to their full-precision floating point counterparts. The most ex…
BinarizationClassification with Binary Weight Networkimage-classificationImage Classification+3Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network
Recently, Frankle & Carbin (2019) demonstrated that randomly-initialized dense networks contain subnetworks that once found can be trained to reach test accuracy comparable to the trained dense network. However, finding …
Classification with Binary Neural NetworkClassification with Binary Weight NetworkQuantization