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Related Work - Garbage Classification

Research Platforms:

  • Google Scholar
  • IEEE
  • Springer Link
  • Papers with code

Literature & Resources:

Minderer, M., Gritsenko, A., Stone, A., Neumann, M., Weissenborn, D., Dosovitskiy, A., Mahendran, A., Arnab, A., Dehghani, M., Shen, Z., Wang, X., Zhai, X., Kipf, T., & Houlsby, N. (o. J.). Simple Open-Vocabulary Object Detection with Vision Transformers. 28.

https://doi.org/10.48550/arXiv.1712.04621 Perez, L., & Wang, J. (2017). The Effectiveness of Data Augmentation in Image Classification using Deep Learning (arXiv:1712.04621). arXiv.

http://arxiv.org/abs/2204.07962 Song, H., Sun, D., Chun, S., Jampani, V., Han, D., Heo, B., Kim, W., & Yang, M.-H. (2022). An Extendable, Efficient and Effective Transformer-based Object Detector (arXiv:2204.07962). arXiv.

http://arxiv.org/abs/2205.01972 Tatsunami, Y., & Taki, M. (2022). Sequencer: Deep LSTM for Image Classification (arXiv:2205.01972). arXiv.

Touvron, H., Cord, M., & Jegou, H. (o. J.). DeiT III: Revenge of the ViT. 27.

https://ieeexplore.ieee.org/abstract/document/9144549 Z. Kang, J. Yang, G. Li and Z. Zhang, "An Automatic Garbage Classification System Based on Deep Learning," in IEEE Access, vol. 8, pp. 140019-140029, 2020, doi: 10.1109/ACCESS.2020.3010496.

https://link.springer.com/article/10.1007/s40747-021-00529-0 Gupta, T., Joshi, R., Mukhopadhyay, D. et al. A deep learning approach based hardware solution to categorise garbage in environment. Complex Intell. Syst. 8, 1129–1152 (2022).