An input signal x is filtered by a collection of dilated band-pass wavelets obtained from ψ, followed by a modulus and finally averaged by a dilation of φ. The wavelets we chose decompose the …

In the context of deriving rotationally invariant representations, the Fourier Transform is particularly appealing since it exhibits invariance to rotational deformations up to phase (a truly …

It is implemented with a deep convolution net-work, which computes successive wavelet transforms and modulus non-linearities. Invariants to scaling, shearing and small deformations …

Traditional approaches to this problem such as those based on Kruppa's equations or the modulus constraint rely explicitly on the knowledge of multiple fundamental matrices or a …

In Bruna’s work, the modulus is obtained from each wavelet coefficient at different levels. In DAWN architecture, the details coefficients per level of the wavelet transform are carried out to …

As a steerability consistent way of normalizing circular harmonics, we propose to ade-quately normalize their complex modulus. The proper scale follows from k k2 = kRe [ ]k2

, Ktx], (7) Here, we note that the text embedding before augmentation [vj mod M, cy] is a concatenation of the prompt embedding vj mod M and the class embedding cy. Recall that M …

They instead take the modulus of the response to a bank of discretely rotated and scaled wavelets, repeatedly in a deep fashion. This is perhaps the most successful version of a …

For instance, while an individual CNT has an elastic modulus of around 1 TPa, a CNT forest’s compressive elastic modulus are frequently on the order of 1-10 MPa [26], akin to nat-ural rubber.

Unlike typical CNNs: 1) regarding the architecture, convolutional layers are defined by fixed wavelet filters, with modulus-based nonlinearity, but without subsequent pooling; 2) regarding …