Authors: Tian Qiu, Wenxiang Xu, Lin Chen, Linyun Zhou, Zunlei Feng, Mingli Song
Affiliation: Zhejiang University
Links: [Paper] | [Poster]

Artificial Neural Networks (ANNs) have gained widespread applications across various areas in recent years. The ANN design was initially inspired by principles of biology. The biological neural network's fundamental response process comprises information transmission and aggregation. The information transmission in biological neurons is often achieved by triggering action potentials that propagate through axons. ANNs utilize activation mechanisms to simulate such biological behavior. However, previous studies have only considered static response conditions, while the biological neuron's response conditions are typically dynamic, depending on multiple factors such as neuronal properties and the real-time environment. Therefore, the dynamic response conditions of biological neurons could help improve the static ones of existing activations in ANNs. Additionally, the biological neuron's aggregated response exhibits high specificity for different categories, allowing the nervous system to differentiate and identify objects. Inspired by these biological patterns, we propose a novel Dynamic Neural Response Tuning (DNRT) mechanism, which aligns the response patterns of ANNs with those of biological neurons. DNRT comprises Response-Adaptive Activation (RAA) and Aggregated Response Regularization (ARR), mimicking the biological neuron's information transmission and aggregation behaviors. RAA dynamically adjusts the response condition based on the characteristics and strength of the input signal. ARR is devised to enhance the network's ability to learn category specificity by imposing constraints on the network's response distribution. Extensive experimental studies indicate that the proposed DNRT is highly interpretable, applicable to various mainstream network architectures, and can achieve remarkable performance compared with existing neural response mechanisms in multiple tasks and domains.

The development environment of this project is python 3.7 & pytorch 1.11.0+cu113.

1. Create your conda environment.

conda create -n qtcls python==3.7 -y

2. Enter your conda environment.

conda activate qtcls

3. Install PyTorch.

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 --extra-index-url https://download.pytorch.org/whl/cu113

Or you can refer to PyTorch to install newer or older versions. Please note that if pytorch ≥ 1.13, then python ≥ 3.8 is required.

4. Install necessary dependencies.

pip install -r requirements.txt

Import the config file (.py) from configs.

single-gpu

python main.py --config /path/to/config.py

or

python main.py -c /path/to/config.py

multi-gpu

torchrun --nproc_per_node=$num_gpus main.py --config /path/to/config.py

or

torchrun --nproc_per_node=$num_gpus main.py -c /path/to/config.py

Currently, the cifar10 and cifar100 datasets will be automatically downloaded to the --data_root directory. Please keep the network accessible. For other datasets, please refer to "How to put your datasets".

During training, the config file, checkpoints (.pth), logs, and other outputs will be stored in --output_dir.

single-gpu

python main.py --config /path/to/config.py --resume /path/to/checkpoint.pth --eval

or

python main.py -c /path/to/config.py -r /path/to/checkpoint.pth --eval

multi-gpu

torchrun --nproc_per_node=$num_gpus main.py --config /path/to/config.py --resume /path/to/checkpoint.pth --eval

or

torchrun --nproc_per_node=$num_gpus main.py -c /path/to/config.py -r /path/to/checkpoint.pth --eval

Our code is released under the Apache 2.0 license. Please see the LICENSE file for more information.

Copyright (c) QIU Tian and ZJU-VIPA Lab. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

If you find the paper useful in your research, please consider citing:

@inproceedings{dnrt,
  title={Dynamic Neural Response Tuning},
  author={Qiu, Tian and Xu, Wenxiang and Chen, Lin and Zhou, Linyun and Feng, Zunlei and Song, Mingli},
  booktitle={International Conference on Learning Representations},
  year={2024}
}