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Fine-Grained Neural Architecture Search
[article]
<span title="2019-11-18">2019</span>
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arXiv
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We present an elegant framework of fine-grained neural architecture search (FGNAS), which allows to employ multiple heterogeneous operations within a single layer and can even generate compositional feature ...
FGNAS runs efficiently in spite of significantly large search space compared to other methods because it trains networks end-to-end by a stochastic gradient descent method. ...
Figure 1 illustrates the proposed fine-grained neural architecture search (FGNAS) approach, where our per-channel search algorithm generates a feature map given by a composition of multiple operations ...
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AtomNAS: Fine-Grained End-to-End Neural Architecture Search
[article]
<span title="2020-02-23">2020</span>
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arXiv
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<span title="2019-12-20">2019</span>
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Search space design is very critical to neural architecture search (NAS) algorithms. ...
We propose a fine-grained search space comprised of atomic blocks, a minimal search unit that is much smaller than the ones used in recent NAS algorithms. ...
ATOMNAS We formulate our neural architecture search method in a fine-grained search space with the atomic block used as the basic search unit. ...
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FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining
[article]
<span title="2021-03-30">2021</span>
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arXiv
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Neural Architecture Search (NAS) yields state-of-the-art neural networks that outperform their best manually-designed counterparts. ...
To address this, we present Neural Architecture-Recipe Search (NARS) to search both (a) architectures and (b) their corresponding training recipes, simultaneously. ...
Fine-grained search improvements. ...
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FGATR-Net: Automatic Network Architecture Design for Fine-Grained Aircraft Type Recognition in Remote Sensing Images
<span title="2020-12-21">2020</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a>
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In this paper, inspired by Neural Architecture Search (NAS), we explore a novel differentiable automatic architecture design framework for fine-grained aircraft type recognition in remote sensing images ...
When all differentiable search phases are finished, the searched model called Fine-Grained Aircraft Type Recognition Net (FGATR-Net) is obtained. ...
Conclusions In this article, a novel automatic architecture design framework for remote sensing fine-grained aircraft type recognition is firstly explored. ...
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EfficientTDNN: Efficient Architecture Search for Speaker Recognition
[article]
<span title="2021-11-24">2021</span>
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arXiv
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<span title="2021-04-20">2021</span>
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<span title="2021-03-31">2021</span>
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<span title="2021-03-25">2021</span>
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Compared with hand-designed approaches, neural architecture search (NAS) appears as a practical technique in automating the manual architecture design process and has attracted increasing interest in spoken ...
In this paper, we propose EfficientTDNN, an efficient architecture search framework consisting of a TDNN-based supernet and a TDNN-NAS algorithm. ...
Neural Architecture Search The NAS technique provides a systematic methodology that designs neural architecture automatically. Zoph et al. ...
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Full-attention based Neural Architecture Search using Context Auto-regression
[article]
<span title="2021-11-13">2021</span>
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arXiv
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<span class="release-stage" >pre-print</span>
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Meanwhile, neural architecture search (NAS) has significantly advanced the automatic design of neural architectures. ...
To verify the efficacy of the proposed methods, we conducted extensive experiments on various learning tasks, including image classification, fine-grained image recognition, and zero-shot image retrieval ...
To eliminate such extensive engineering, neural architecture search (NAS) methods [1] - [4] have been proposed to automate the design of neural architectures. ...
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Fine-Grained Stochastic Architecture Search
[article]
<span title="2020-06-17">2020</span>
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arXiv
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In this paper, we introduce Fine-Grained Stochastic Architecture Search (FiGS), a differentiable search method that searches over a much larger set of candidate architectures. ...
Differentiable neural architecture search (DNAS) methods reduce the search cost but explore a limited subspace of candidate architectures. ...
Related Work Neural architecture search (NAS) automates the design of neural net models with machine learning. ...
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VEGA: Towards an End-to-End Configurable AutoML Pipeline
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<span title="2020-11-26">2020</span>
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arXiv
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<span title="2020-11-04">2020</span>
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Architecture Search (NAS), Hyperparameter Optimization (HPO), Auto Data Augmentation, Model Compression, and Fully Train. b) To support a variety of search algorithms and tasks, we design a novel fine-grained ...
we present VEGA, an efficient and comprehensive AutoML framework that is compatible and optimized for multiple hardware platforms. a) The VEGA pipeline integrates various modules of AutoML, including Neural ...
With fine-grained search space introduced in Section 4.2, model compression can be view as a special case of neural architecture search. ...
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Differentiable Fine-grained Quantization for Deep Neural Network Compression
[article]
<span title="2018-11-13">2018</span>
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arXiv
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In this work, we propose a fine-grained quantization approach for deep neural network compression by relaxing the search space of quantization bitwidth from discrete to a continuous domain. ...
Neural networks have shown great performance in cognitive tasks. When deploying network models on mobile devices with limited resources, weight quantization has been widely adopted. ...
To achieve this, we propose a fine-grained quantization approach that relaxes the search space of quantization bitwidth from discrete to continuous domain and applies gradient descend optimization to generate ...
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A Systematic Evaluation of Recent Deep Learning Architectures for Fine-Grained Vehicle Classification
[article]
<span title="2018-06-08">2018</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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task of fine-grained classification of vehicles. ...
Fine-grained vehicle classification is the task of classifying make, model, and year of a vehicle. ...
Fine-grained vehicle classification finds application in vehicle search and tracking, as well as traffic analysis. ...
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Learning Fine-Grained Image Similarity with Deep Ranking
<span title="">2014</span>
<i title="IEEE">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2014 IEEE Conference on Computer Vision and Pattern Recognition</a>
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Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. ...
The deep ranking model employs a triplet-based hinge loss ranking function to characterize fine-grained image similarity relationships, and a multiscale neural network architecture to capture both the ...
Search-by-example requires the distinction of differences between images within the same category, i.e., fine-grained image similarity. ...
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Learning Fine-grained Image Similarity with Deep Ranking
[article]
<span title="2014-04-17">2014</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. ...
The deep ranking model employs a triplet-based hinge loss ranking function to characterize fine-grained image similarity relationships, and a multiscale neural network architecture to capture both the ...
Search-by-example requires the distinction of differences between images within the same category, i.e., fine-grained image similarity. ...
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Graph-RISE: Graph-Regularized Image Semantic Embedding
[article]
<span title="2019-02-14">2019</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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) ultra-fine-grained semantic labels. ...
Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering. ...
We refer to ultra fine-grained as "instance-level" to contrast with category-level and fine-grained semantics. ...
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Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
[article]
<span title="2020-08-13">2020</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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Other Versions
<span title="2020-07-31">2020</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.16100v1">arXiv:2007.16100v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qy52yfkkv5ho5o6m5enzdydckm">fatcat:qy52yfkkv5ho5o6m5enzdydckm</a> </span>
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To explore the spectrum of efficient 3D models, we first define a flexible architecture design space based on SPVConv, and we then present 3D Neural Architecture Search (3D-NAS) to search the optimal network ...
With negligible overhead, this point-based branch is able to preserve the fine details even from large outdoor scenes. ...
Fig. 3 . 3 Overview of 3D Neural Architecture Search (3D-NAS): we first train a super network composed of multiple SPVConv's, supporting fine-grained channel numbers and elastic network depths. ...
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Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search
[article]
<span title="2021-12-09">2021</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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This paper bypasses existing 2D architectures, and directly searched for 3D architectures in a fine-grained space, where block type, filter number, expansion ratio and attention block are jointly searched ...
A probabilistic neural architecture search method is adopted to efficiently search in such a large space. ...
Neural architecture search (NAS) aims to replace the la-
• A fine-grained design space for efficient video recogni- borious human design of network architectures, as well as
tion is proposed, to ...
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