Hypothesis (L TH): a randomly-initialized dense neural network contains an extremely sparse subnet-work (i.e., a winning lottery ticket) such that, when trained from scratch with weights being reset to its initialization, can achieve similar performance to the original dense network within similar

A natural idea is to compute the solution path w.r.t.

Recently, Vision Transformer and its variants have shown great promise on various computer vision tasks. The ability of capturing local and global visual dependencies through self-attention is the key to its success.

Thus, TINL is independent of QINL in terms of the object of study.

Bird's-Eye View (BEV) maps provide a structured, top-down abstraction that is crucial for autonomous-driving perception. In this work, we employ Cross-View Transformers (CVT) for learning to map camera images to three BEV's channels - road, lane markings, and planned trajectory - using a realistic simulator for urban driving. Our study examines generalization to unseen towns, the effect of different camera layouts, and two loss formulations (focal and L1). Using training data from only a town, a four-camera CVT trained with the L1 loss delivers the most robust test performance, evaluated in a new town. Overall, our results underscore CVT's promise for mapping camera inputs to reasonably accurate BEV maps.

To conserve the template mesh's topological properties, we train

We structure this paper as follows: In Sec. 2, we briefly review the celebrated FMM and establish

Deep directed generative models with energy-based probability estimation.