No abstract

Almost all visual transformers such as ViT [14] or DeiT [41] rely on predefined positional encodings to incorporate the order of each input token. These encodings are often implemented as learnable fixed-dimension vectors or sinusoidal functions of different frequencies, which are not possible to accommodate variable-length input sequences. This inevitably limits a wider application of transformers in vision, where many tasks require changing the input size onthe-fly.In this paper, we propose to employ an implicit conditional position encodings scheme, which is conditioned on the local neighborhood of the input token. It is effortlessly implemented as what we call Position Encoding Generator (PEG), which can be seamlessly incorporated into the current transformer framework. Our new model with PEG is named Conditional Position encodings Visual Transformer (CPVT) and can naturally process the input sequences of arbitrary length. We demonstrate that CPVT can result in visually similar attention maps and even better performance than those with predefined positional encodings. We obtain state-of-the-art results on the ImageNet classification task compared with visual Transformers to date. Our code will be made available at https://github.com/ Meituan-AutoML/CPVT .

The ability to rank models by its real strength is the key to Neural Architecture Search. Traditional approaches adopt an incomplete training for such purpose which is still very costly. One-shot methods are thus devised to cut the expense by reusing the same set of weights. However, it is uncertain whether shared weights are truly effective. It is also unclear if a picked model is better because of its vigorous representational power or simply because it is overtrained.In order to remove the suspicion, we propose a novel idea called Fair Neural Architecture Search (FairNAS), in which a strict fairness constraint is enforced for fair inheritance and training. In this way, our supernet exhibits nice convergence and very high training accuracy. The performance of any sampled model loaded with shared weights from the supernet strongly correlates with that of stand-alone counterpart when trained fully. This result dramatically improves the searching efficiency, with a multi-objective reinforced evolutionary search backend, our pipeline generated a new set of state-of-the-art architectures on ImageNet: FairNAS-A attains 75.34% top-1 validation accuracy on ImageNet, FairNAS-B 75.10%, FairNAS-C 74.69%, even with lower multi-adds and/or fewer number of parameters compared with others. The models and their evaluation code are made publicly available online 1 .

No abstract

No abstract

No abstract

Deep convolution neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by using much deeper layers, which are not friendly to constrained resources. Pursuing a trade-off between the restoration capacity and the simplicity of models is still non-trivial. Recent contributions are struggling to manually maximize this balance, while our work achieves the same goal automatically with neural architecture search. Specifically, we handle superresolution with a multi-objective approach. We also propose an elastic search tactic at both micro and macro level, based on a hybrid controller that profits from evolutionary computation and reinforcement learning. Quantitative experiments help us to draw a conclusion that our generated models dominate most of the state-of-the-art methods with respect to the individual FLOPS.

No abstract