DetNAS: Neural Architecture Search on Object Detection

By Yukang Chen, Tong Yang, Xiangyu Zhang, Gaofeng Meng, Chunhong Pan. 2019

94 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neural Architecture Search & AutoML

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In Computer Vision and Pattern Recognition

In Neural Architecture Search & AutoML

Citations in the corpus

A Comprehensive Survey of Neural Architecture Search (2020, methodology): “With the improvement of NAS search efficiency, NAS is also quickly applied in the fields of object detection [65, 75, 111, 118], semantic segmentation [63, 64, 120], adversarial learning [53], architectural scaling [114, 122, 124], multi-objective optimization [39, 115, 125], platform-aware [28, 34,”
GreedyNAS: Towards Fast One-Shot NAS With Greedy Supernet (2020, background): “In contrast, neural architecture search (NAS) aims at automatically learning the network architecture to further boost the performance for target tasks [10, 20, 43, 2, 19].”
DSNAS: Direct Neural Architecture Search Without Parameter Retraining (2020, background): “semantic segmentation [12] and object detection [4] etc.”
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search (2020, methodology): “ShufﬂeNetV2-20 (Chen et al., 2019b) RetinaNet 13.19M 132.76B 32.1 MobileNetV2 (Sandler et al., 2018) 11.49M 133.05B 32.8 DetNAS (Chen et al., 2019b) 13.41M 133.26B 33.3 FNA 11.73M 133.03B 33.9 MobileNetV2 (Sandler et al., 2018) SSDLite 4.3M 0.8B 22.1 Mnasnet-92 (Tan et al., 2018) 5.3M 1.0B 22.9 FNA ”
Efficient Structured Pruning and Architecture Searching for Group Convolution (2018, neutral): “Evolutionary algorithm is another option [33, 1, 22].”
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking (2019, background): “Neural architecture search (NAS) has recently attracted significant interests due to its capability of automating neural network architecture design and its success in outperforming hand-crafted architectures in many important tasks such as image classification [1], object detection [2], and semanti”
DATA: Differentiable ArchiTecture Approximation (2019, unknown): “, evolution-based NAS [13, 18, 26, 41, 43, 44, 45, 47], reinforcement learning-based NAS [2, 3, 21, 42, 56, 59, 60], and gradient-based NAS [11, 34, 35, 46, 53], which has achieved significant successes in a multitude of fields, including image classification [4, 12, 21, 30, 31, 34, 44, 53, 60], sem”
Sample-Efficient Neural Architecture Search by Learning Action Space (2019, unknown): “Architectures found by NAS have achieved remarkable results in image classification [1, 2], object detection and segmentation [3, 4, 5], as well as other domains such as language tasks [6, 7].”
Densely Connected Search Space for More Flexible Neural Architecture Search (2019, background): “Det-NAS [7] is a recent work that aims at searching the back-bone architectures directly on object detection.”
Recent Advances in Deep Learning for Object Detection (2019, background): “vant work has been proposed to address detection problems via AutoML [249, 250], such as learning FPN structure [250] and learning data augmentation policies [251], which show significant improvement over the baselines.”
Computation Reallocation for Object Detection (2019, methodology): “There are some work use NAS methods on object detection task (Chen et al., 2019; Ning Wang & Shen, 2019; Ghiasi et al., 2019).”
Convolutional neural network: a review of models, methodologies and applications to object detection (2019, unknown): “DetNAS 2019 Yukang Chen [111] Achieves 40.”
Deep Learning for Generic Object Detection: A Survey (2018, methodology): “ural to consider automated design of detection backbone architectures, such as the recent Automated Machine Learning (AutoML) [219], which has been applied to image classiﬁcation and object detection [22,39,80,171,331, 332]. (5) Object Instance Segmentation: For a richer and more detailed understand”
NAS-FCOS: Fast Neural Architecture Search for Object Detection (2019, methodology): “DetNAS [2] follows this idea to search for an efﬁcient object detection architecture.”
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending (2020, methodology): “l ResNet18/50, the position of doubling the channel size block is xed at the beginning of each stage. For example, as shown in Figure 3, the backbone architecture encoding string looks like \BB 64 13 [5, 9] [7, 12]" where the rst placeholder encodes the block setting, 64 is the base channel siz”
Multi-objective Neural Architecture Search via Predictive Network Performance Optimization (2019, unknown): “Graph neural network has been proposed in previous work for predicting the parameters of the architecture using a graph hypernetwork (Zhang et al., 2019).”