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Training Deeper Convolutional Networks with Deep Supervision

arXiv 1505.02496 ↗First posted 11 May 2015cs.CVcited by 36

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Abstract (arXiv)

One of the most promising ways of improving the performance of deep convolutional neural networks is by increasing the number of convolutional layers. However, adding layers makes training more difficult and computationally expensive. In order to train deeper networks, we propose to add auxiliary supervision branches after certain intermediate layers during training. We formulate a simple rule of thumb to determine where these branches should be added. The resulting deeply supervised structure makes the training much easier and also produces better classification results on ImageNet and the recently released, larger MIT Places dataset

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2015.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 7; 2024: 8; 2025: 7; 2026: 3.

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  11. 20243DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN Networkscited by 7
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  9. 2021Uncertainty, Edge, and Reverse-Attention Guided Generative Adversarial Network for Automatic Building Detection in Remotely Sensed Imagescited by 1
  10. 2022DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scanscited by 1
  11. 2025VPNeXt -- Rethinking Dense Decoding for Plain Vision Transformercited by 1
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