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WSNet: Compact and Efficient Networks Through Weight Sampling
Xiaojie Jin · Yingzhen Yang · Ning Xu · Jianchao Yang · Nebojsa Jojic · Jiashi Feng · Shuicheng Yan

Fri Jul 13 09:15 AM -- 12:00 PM (PDT) @ Hall B #6

We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks. Existing approaches conventionally learn full model parameters independently and then compress them via ad hoc processing such as model pruning or filter factorization. Alternatively, WSNet proposes learning model parameters by sampling from a compact set of learnable parameters, which naturally enforces parameter sharing throughout the learning process. We demonstrate that such a novel weight sampling approach (and induced WSNet) promotes both weights and computation sharing favorably. By employing this method, we can more efficiently learn much smaller networks with competitive performance compared to baseline networks with equal numbers of convolution filters. Specifically, we consider learning compact and efficient 1D convolutional neural networks for audio classification. Extensive experiments on multiple audio classification datasets verify the effectiveness of WSNet. Combined with weight quantization, the resulted models are up to 180x smaller and theoretically up to 16x faster than the well-established baselines, without noticeable performance drop.

Author Information

Xiaojie Jin (National University of Singapore)
Yingzhen Yang (University of Illinois at Urbana-Champaign)
Ning Xu (Snap)
Jianchao Yang (Bytedance Inc.)
Nebojsa Jojic (Microsoft Research)
Jiashi Feng (National University of Singapore)
Shuicheng Yan (Qihoo/360)

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