NullSpaceNet：具有可微损失函数的零空间卷积神经网络
NullSpaceNet: Nullspace Convoluional Neural Network with Differentiable Loss Function
摘要：
我们提出了NullSpaceNet，这是一种新的网络，它从像素级输入映射到联合零空间（与传统的特征空间相反），其中新学习的联合零空间特征具有更清晰的解释并且更可分离。NullSpaceNet确保来自同一个类的所有输入都被折叠到这个新的联合零空间中的一个点，而不同的类被折叠到具有高分离裕度的不同点。此外，提出了一种新的可微损失函数，具有封闭形式的解决方案，没有自由参数。NullSpaceNet在4个不同数据集上对具有全连接层的VGG16进行测试时表现出卓越的性能，准确率提高高达4.55%，可学习参数从135M减少到19M，推理时间减少99%，有利于NullSpaceNet。这意味着NullSpaceNet只需要不到传统CNN 1%的时间就能以更好的准确度对一批图像进行分类。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
