Robust Training of Neural Networks at Arbitrary Precision and Sparsity

The discontinuous operations inherent in quantization and sparsification introduce obstacles to backpropagation. This is particularly challenging when training deep neural networks in ultra-low precision and sparse regimes. We propose a novel, robust, and universal solution: a denoising affine transform that stabilizes training under these challenging conditions. By formulating quantization and sparsification as perturbations during training, we derive a perturbation-resilient approach based on ridge regression. Our solution employs a piecewise constant backbone model to ensure a performance lower bound and features an inherent noise reduction mechanism to mitigate perturbation-induced corruption. This formulation allows existing models to be trained at arbitrarily low precision and sparsity levels with off-the-shelf recipes. Furthermore, our method provides a novel perspective on training temporal binary neural networks, contributing to ongoing efforts to narrow the gap between artificial and biological neural networks.

Multi-path Neural Networks for On-device Multi-domain Visual Classification

Learning multiple domains/tasks with a single model is important for improving data efficiency and lowering inference cost for numerous vision tasks, especially on resource-constrained mobile devices. However, hand-crafting a multi-domain/task model can be both tedious and challenging. This paper proposes a novel approach to automatically learn a multi-path network for multi-domain visual classification on mobile devices. The proposed multi-path network is learned from neural architecture search by applying one reinforcement learning controller for each domain to select the best path in the super-network created from a MobileNetV3-like search space. An adaptive balanced domain prioritization algorithm is proposed to balance optimizing the joint model on multiple domains simultaneously. The determined multi-path model selectively shares parameters across domains in shared nodes while keeping domain-specific parameters within non-shared nodes in individual domain paths. This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains. Extensive evaluations on the Visual Decathlon dataset demonstrate that the proposed multi-path model achieves state-of-the-art performance in terms of accuracy, model size, and FLOPS against other approaches using MobileNetV3-like architectures. Furthermore, the proposed method improves average accuracy over learning single-domain models individually, and reduces the total number of parameters and FLOPS by 78% and 32% respectively, compared to the approach that simply bundles single-domain models for multi-domain learning.

Discovering Multi-Hardware Mobile Models via Architecture Search

Developing efficient models for mobile phones or other on-device deployments has been a popular topic in both industry and academia. In such scenarios, it is often convenient to deploy the same model on a diverse set of hardware devices owned by different end users to minimize the costs of development, deployment and maintenance. Despite the importance, designing a single neural network that can perform well on multiple devices is difficult as each device has its own specialty and restrictions: A model optimized for one device may not perform well on another. While most existing work proposes different models optimized for each single hardware, this paper is the first which explores the problem of finding a single model that performs well on multiple hardware. Specifically, we leverage architecture search to help us find the best model, where given a set of diverse hardware to optimize for, we first introduce a multi-hardware search space that is compatible with all examined hardware. Then, to measure the performance of a neural network over multiple hardware, we propose metrics that can characterize the overall latency performance in an average case and worst case scenario. With the multi-hardware search space and new metrics applied to Pixel4 CPU, GPU, DSP and EdgeTPU, we found models that perform on par or better than state-of-the-art (SOTA) models on each of our target accelerators and generalize well on many un-targeted hardware. Comparing with single-hardware searches, multi-hardware search gives a better trade-off between computation cost and model performance.

Can weight sharing outperform random architecture search? An investigation with TuNAS

Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models. There is, however, an ongoing debate whether these efficient methods are significantly better than random search. Here we perform a thorough comparison between efficient and random search methods on a family of progressively larger and more challenging search spaces for image classification and detection on ImageNet and COCO. While the efficacies of both methods are problem-dependent, our experiments demonstrate that there are large, realistic tasks where efficient search methods can provide substantial gains over random search. In addition, we propose and evaluate techniques which improve the quality of searched architectures and reduce the need for manual hyper-parameter tuning. Source code and experiment data are available at https://github.com/google-research/google-research/tree/master/tunas

Geo-Aware Networks for Fine Grained Recognition

Fine grained recognition distinguishes among categories with subtle visual differences. To help identify fine grained categories, other information besides images has been used. However, there has been little effort on using geolocation information to improve fine grained classification accuracy. Our contributions to this field are twofold. First, to the best of our knowledge, this is the first paper which systematically examined various ways of incorporating geolocation information to fine grained images classification - from geolocation priors, to post-processing, to feature modulation. Secondly, to overcome the situation where no fine grained dataset has complete geolocation information, we introduce, and will make public, two fine grained datasets with geolocation by providing complementary information to existing popular datasets - iNaturalist and YFCC100M. Results on these datasets show that, the best geo-aware network can achieve 8.9% top-1 accuracy increase on iNaturalist and 5.9% increase on YFCC100M, compared with image only models' results. In addition, for small image baseline models like Mobilenet V2, the best geo-aware network gives 12.6% higher top-1 accuracy than image only model, achieving even higher performance than Inception V3 models without geolocation. Our work gives incentives to use geolocation information to improve fine grained recognition for both server and on-device models.

Searching for MobileNetV3

We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 15% compared to MobileNetV2. MobileNetV2-Small is 4.6% more accurate while reducing latency by 5% compared to MobileNetV2. MobileNetV3-Large detection is 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation.