Convo Lutions
Found 3 free book(s)
Aggregated Residual Transformations for Deep Neural Networks
openaccess.thecvf.comadopted technique to reduce redundancy of deep convo-lutional networks and accelerate/compress them. Ioan-nou et al. [16] present a “root”-patterned network for re-ducing computation, and branches in the root are realized by grouped convolutions. These methods [6, 18, 21, 16] have shown elegant compromise of accuracy with lower
Lite-HRNet: A Lightweight High-Resolution Network
openaccess.thecvf.comWe find that the heavily-used pointwise (1 × 1) convo-lutions in shuffle blocks become the computational bottle-neck. We introduce a lightweight unit, conditional chan-nel weighting, to replace costly pointwise (1 × 1) convolu-tions in shuffle blocks. The complexity of channel weight-ing is linear w.r.t the number of channels and lower than
An Empirical Study of Training Self-Supervised Vision ...
arxiv.orgthan interlaced with non-degenerated (i.e., non-1 1) convo-lutions.2 This largely closes the architectural gap between NLP and vision. ViT achieves compelling accuracy in su-pervised learning, especially with large-scale data and high-capacity models. Given these properties, we believe ViT is a must-study baseline for self-supervised learning ...