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Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations
Finally, we implicitly demonstrate that, in popular ConvNets, the 2DConv can be replaced with a 3TConv and that the weights can be transferred to yield pretrained 3TConvs.
Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks
It is important that we understand the driving factors behind the predictions, in humans and in deep neural networks.
Temporal Factorization of 3D Convolutional Kernels
3D convolutional neural networks are difficult to train because they are parameter-expensive and data-hungry.
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