We study the function space characterization of the inductive bias resulting from controlling the $\ell_2$ norm of the weights in linear convolutional networks. We view this in terms of an induced regularizer in the function space given by the minimum norm of weights required to realize a linear function. For two layer linear convolutional networks with $C$ output channels and kernel size $K$, we show the following: (a) If the inputs to the network have a single channel, the induced regularizer for any $K$ is a norm given by a semidefinite program (SDP) that is independent of the number of output channels $C$. (b) In contrast, for networks with multi-channel inputs, multiple output channels can be necessary to merely realize all matrix-valued linear functions and thus the inductive bias does depend on $C$. Further, for sufficiently large $C$, the induced regularizer for $K=1$ and $K=D$ are the nuclear norm and the $\ell_{2,1}$ group-sparse norm, respectively, of the Fourier coefficients. (c) Complementing our theoretical results, we show through experiments on MNIST and CIFAR-10 that our key findings extend to implicit biases from gradient descent in overparameterized networks.
翻译:我们研究了控制线性革命网络重量的值值为$_2美元标准而形成的带宽偏差的函数空间特征。我们从一个根据实现线性函数所需的最小重量标准在功能空间中诱导的正统性角度来看待这一点。对于两个具有美元输出渠道和内核大小为KK美元的层线性革命网络,我们展示了以下内容:(a)如果对网络的投入有一个单一渠道,任何K美元诱导的正则是一个规范,一个独立于产出渠道数目的半限定程序(SDP)给出了该规范。 (b)相反,对于具有多通道投入的网络来说,多输出渠道可能只是需要实现所有基数值线性功能,因此内向偏差取决于C美元。 此外,对于足够大的C美元而言,以1美元和1美元为单位的诱导调节器是核规范,而以美元为单位的正值为1美元,以美元为美元为单位的正值为美元,以美元为单位的正值计算出一个规范。