Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.
Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.
We build a family of models that surpass existing MLPs and achieve a comparable accuracy (83. 2%) on ImageNet-1K classification compared to the state-of-the-art Transformer such as Swin Transformer (83. 3%) but using fewer parameters and FLOPs.
Ranked #140 on Image Classification on ImageNet
As a result, over 1% of the unprompted output of language models trained on these datasets is copied verbatim from the training data.
We initialise the GPSA layers to mimic the locality of convolutional layers, then give each attention head the freedom to escape locality by adjusting a gating parameter regulating the attention paid to position versus content information.
Ranked #168 on Image Classification on ImageNet
Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.
We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX.
Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens.
Ranked #1 on Image-to-Text Retrieval on Flickr30k