Deep-Wide Learning Assistance for Insect Pest Classification
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Abstract (arXiv)
Accurate insect pest recognition plays a critical role in agriculture. It is a challenging problem due to the intricate characteristics of insects. In this paper, we present DeWi, novel learning assistance for insect pest classification. With a one-stage and alternating training strategy, DeWi simultaneously improves several Convolutional Neural Networks in two perspectives: discrimination (by optimizing a triplet margin loss in a supervised training manner) and generalization (via data augmentation). From that, DeWi can learn discriminative and in-depth features of insect pests (deep) yet still generalize well to a large number of insect categories (wide). Experimental results show that DeWi achieves the highest performances on two insect pest classification benchmarks (76.44% accuracy on the IP102 dataset and 99.79% accuracy on the D0 dataset, respectively). In addition, extensive evaluations and ablation studies are conducted to thoroughly investigate our DeWi and demonstrate its superiority. Our source code is available at https://github.com/toannguyen1904/DeWi.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 39 of its 104 references that are in the map. First posted on arXiv 16 Sep 2024.
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39 of 104 references.
References · 39
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
- 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
- 2016Feature Pyramid Networks for Object Detectioncited by 5,021
27 more
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
- 2016Wide Residual Networkscited by 3,057
- 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
- 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
- 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
- 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
- 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
- 2016An overview of gradient descent optimization algorithmscited by 1,021
- 2019Decoupling Representation and Classifier for Long-Tailed Recognitioncited by 806
- 2017In Defense of the Triplet Loss for Person Re-Identificationcited by 784
- 2020Image Segmentation Using Deep Learning: A Surveycited by 659
- 2020Prototypical Contrastive Learning of Unsupervised Representationscited by 636
- 2020A Survey on Contrastive Self-supervised Learningcited by 553
- 2020Circle Loss: A Unified Perspective of Pair Similarity Optimizationcited by 402
- 2017Residual Attention Network for Image Classificationcited by 385
- 2015Recent Advances in Convolutional Neural Networkscited by 360
- 2020Keystroke Dynamics as Part of Lifeloggingcited by 334
- 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
- 2018Deep learning in agriculture: A surveycited by 160
- 2022A Comprehensive Survey of Image Augmentation Techniques for Deep Learningcited by 95
- 2020AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversariescited by 90
- 2020MixCo: Mix-up Contrastive Learning for Visual Representationcited by 62
- 2023Open-Vocabulary Affordance Detection in 3D Point Cloudscited by 41
- 2020A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resourcescited by 30
- 2023Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understandingcited by 29
- 2021An Efficient Insect Pest Classification Using Multiple Convolutional Neural Network Based Modelscited by 7
- 2021High performing ensemble of convolutional neural networks for insect pest image detectioncited by 3
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