# AdaResNet: Enhancing Residual Networks with Dynamic Weight Adjustment for Improved Feature Integration

arXiv: https://arxiv.org/abs/2408.09958 · HTML: https://mlsanity.com/paper/2408.09958/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [Recurrent Highway Networks](https://mlsanity.com/paper/1607.03474/) (2016)
- [Tailor: Altering Skip Connections for Resource-Efficient Inference](https://mlsanity.com/paper/2301.07247/) (2023)

## Abstract (arXiv)

In very deep neural networks, gradients can become extremely small during backpropagation, making it challenging to train the early layers. ResNet (Residual Network) addresses this issue by enabling gradients to flow directly through the network via skip connections, facilitating the training of much deeper networks. However, in these skip connections, the input ipd is directly added to the transformed data tfd, treating ipd and tfd equally, without adapting to different scenarios. In this paper, we propose AdaResNet (Auto-Adapting Residual Network), which automatically adjusts the ratio between ipd and tfd based on the training data. We introduce a variable, weighttfd^ipd, to represent this ratio. This variable is dynamically adjusted during backpropagation, allowing it to adapt to the training data rather than remaining fixed. Experimental results demonstrate that AdaResNet achieves a maximum accuracy improvement of over 50% compared to traditional ResNet.