In recent years an increasing number of researchers and practitioners have
been suggesting algorithms for large-scale neural network architecture search:
genetic algorithms, reinforcement learning, learning curve extrapolation, and
accuracy predictors. None of them, however, demonstrated high-performance
without training new experiments in the presence of unseen datasets. We propose
a new deep neural network accuracy predictor, that estimates in fractions of a
second classification performance for unseen input datasets, without training.
In contrast to previously proposed approaches, our prediction is not only
calibrated on the topological network information, but also on the
characterization of the dataset-difficulty which allows us to re-tune the
prediction without any training. Our predictor achieves a performance which
exceeds 100 networks per second on a single GPU, thus creating the opportunity
to perform large-scale architecture search within a few minutes. We present
results of two searches performed in 400 seconds on a single GPU. Our best
discovered networks reach 93.67% accuracy for CIFAR-10 and 81.01% for
CIFAR-100, verified by training. These networks are performance competitive
with other automatically discovered state-of-the-art networks however we only
needed a small fraction of the time to solution and computational resources.

该研究提出了一种新的深度神经网络准确性预测器，可以预测未知输入数据集的分类性能，在不需要任何训练的情况下，在单个 GPU 上每秒超过 100 个网络，大规模架构搜索只需要几分钟。

TAPAS: 用于架构搜索的无需训练的准确性预测器

TAPAS: Train-less Accuracy Predictor for Architecture Search

We propose a novel hashing-based matching scheme, called Locally Optimized
Hashing (LOH), based on a state-of-the-art quantization algorithm that can be
used for efficient, large-scale search, recommendation, clustering, and
deduplication. We show that matching with LOH only requires set intersections
and summations to compute and so is easily implemented in generic distributed
computing systems. We further show application of LOH to: a) large-scale search
tasks where performance is on par with other state-of-the-art hashing
approaches; b) large-scale recommendation where queries consisting of thousands
of images can be used to generate accurate recommendations from collections of
hundreds of millions of images; and c) efficient clustering with a graph-based
algorithm that can be scaled to massive collections in a distributed
environment or can be used for deduplication for small collections, like search
results, performing better than traditional hashing approaches while only
requiring a few milliseconds to run. In this paper we experiment on datasets of
up to 100 million images, but in practice our system can scale to larger
collections and can be used for other types of data that have a vector
representation in a Euclidean space.

本文提出了一种新的哈希相似匹配方案 —— 局部优化哈希（LOH），该方案是基于一种最先进的量化算法的。LOH 可用于高效的大规模搜索、推荐、聚类和去重。我们进一步展示了 LOH 的应用：a）大规模搜索任务中，性能与其他最先进的哈希方式相当；b）大规模推荐中，可使用由数千幅图像组成的查询，从数亿图像的集合中生成准确的推荐；c）利用基于图形的算法进行高效聚类，该算法可在分布式环境中扩展到海量收藏品，或用于小集合的去重，如搜索结果，其性能优于传统哈希方案，仅需几毫秒运行。在本文中，我们在最多 1 亿张图片的数据集上进行实验，但实际上我们的系统可以扩展到更大的收藏品，并可用于在欧几里得空间中具有向量表示的其他类型的数据。