论文标题
NullSpaceNet：具有可区分损失功能的NullSpace综合神经网络
NullSpaceNet: Nullspace Convoluional Neural Network with Differentiable Loss Function
论文作者
论文摘要
我们提出了一个新型网络NullSpaceNet，该网络从像素级输入到联合空格（与传统特征空间相对），新学习的联合零Space功能具有更清晰的解释，并且更可分开。 NullSpaceNet确保将同一类中的所有输入折叠成这个新的nullSpace中的一个点，并且不同类别被折叠成具有高分离边缘的不同点。此外，提出了一种新型的可区分损耗函数，该损失函数具有没有自由参数的封闭式解决方案。 NullSpaceNet在4个不同的数据集上使用完全连接的VGG16进行测试时，表现出卓越的性能，准确性增益高达4.55％，可学习参数从1.35m降低到19m，而推理时间减少了99％，以支持NullSpaceNet。这意味着NullSpaceNet所需的时间不到1％，而传统的CNN则以更好的准确性对一批图像进行分类。
We propose NullSpaceNet, a novel network that maps from the pixel level input to a joint-nullspace (as opposed to the traditional feature space), where the newly learned joint-nullspace features have clearer interpretation and are more separable. NullSpaceNet ensures that all inputs from the same class are collapsed into one point in this new joint-nullspace, and the different classes are collapsed into different points with high separation margins. Moreover, a novel differentiable loss function is proposed that has a closed-form solution with no free-parameters. NullSpaceNet exhibits superior performance when tested against VGG16 with fully-connected layer over 4 different datasets, with accuracy gain of up to 4.55%, a reduction in learnable parameters from 135M to 19M, and reduction in inference time of 99% in favor of NullSpaceNet. This means that NullSpaceNet needs less than 1% of the time it takes a traditional CNN to classify a batch of images with better accuracy.
