Sentence examples for train of images from inspiring English sources

From the lingerie whiteness of Peter Copping's poplin blouses and skirts for Nina Ricci to the human force of Rick Owens's burly step dancers to the shiny candy-wrapper dresses at Lanvin, it's a jolting train of images.

You're probably familiar with its outlines, even if you've never read a word of Proust: the grownup narrator eats a madeleine dipped in tea, and it reminds him so much of childhood afternoons at his aunt's home in Combray that his mind summons a freight train of images and stories from decades earlier in his life, sparking more than 3,000 pages of recollections.

The work presented in [35] depends on the active training of images represented by Haar-like features.

The training of image-based facial animation system is time consuming and can only find one of the possible optimal parameters [1, 11], such that the facial animation system can only achieve good quality for a limited set of sentences.

This is why neural networks trained on images of past U.S. presidents predicted Donald Trump would win the upcoming U.S. election, after being trained with images of past (male) presidents.

The team also notes that their model is more than just a really complex parrot that spits back entries from its training set of images.

This is a logical conclusion since the Sipi model is based on a training set of images that represent specifically the class of images to which Toulouse belongs.

This sub-space is defined by a set of representative basis vectors, which are learned using a training set of images.

A real-life transmission system will therefore utilize a model calculated offline based on a training set of images, which we address as the source-independent model.

The distortion characteristics of the source-independent model are based on a training set of images: we first compute the components, and as described in Section 2, which correspond to the individual source models for each training image.

This training database of images was used for optimization of several thresholds in the sequential classification algorithm.