LTC: Improving Deep Representation Learning via Auxiliary Learnable Target Coding

This is a Pytorch implementation of the paper LTC.

Deep representation learning is a subfield of machine learning that focuses on learning meaningful and useful representations of data through deep neural networks. However, existing methods for semantic classification typically employ pre-defined target codes such as the one-hot and the Hadamard codes, which can either fail or be less flexible to model inter-class correlation. In light of this, this paper introduces a novel learnable target coding as an auxiliary regularization of deep representation learning, which can not only incorporate latent dependency across classes but also impose geometric properties of target codes into representation space. Specifically, a margin-based triplet loss and a correlation consistency loss on the proposed target codes are designed to encourage more discriminative representations owing to enlarging between-class margins in representation space and favoring equal semantic correlation of learnable target codes respectively.

For more details, please refer to our paper.

Method Overview

Setup

Install Package Dependencies

torch torchvision
PyYAML easydict tqdm scikit-learn efficientnet_pytorch pandas opencv-python colorama tensorboardX

Datasets

Download CUB-200-2011, Stanford Cars, and FGVC-Aircraft datasets to ./data, or change the data root in the ./datasets/xxx.py for each dataset.

Training & Evaluation

To train a baseline on the CUB dataset with the Resnet18 backbone.

python main.py --dataset cub --netname resnet18 --seed 1

To train the proposed learnable target coding method on the CUB dataset with the Resnet18 backbone.

python main.py --dataset cub --netname resnet18 --seed 1 --LTC

To train the Hadamard target coding method on the CUB dataset with the Resnet18 backbone.

python main.py --dataset cub --netname resnet18 --seed 1 --HTC

To train model on other datasets using other network backbones, you can specify the following arguments:

--netname: name of network architecture (for example: resnet34, resnet50, ...)

--dataset: name of dataset (for example: cub, car, aircrft, ...)

More hype-parameters are introduced in the ./utils/conf.py.

Performance

Performance compared with SOTA methods on CUB-200-2011

Method	Backbone	CUB (%)	CAR (%)	AIR (%)
Baseline	ResNet18	81.88	90.45	88.60
DTRG	ResNet18	84.52	91.99	90.28
HTC	ResNet18	83.19	91.54	88.93
LTC (Ours)	ResNet18	84.07	92.67	90.91
Baseline	ResNet34	84.82	91.94	89.98
DTRG	ResNet34	85.66	93.32	91.54
HTC	ResNet34	85.35	92.97	91.33
LTC (Ours)	ResNet34	85.55	94.08	92.35
Baseline	ResNet50	85.46	92.89	90.97
DTRG	ResNet50	87.29	94.31	92.12
HTC	ResNet50	86.33	93.92	91.36
LTC (Ours)	ResNet50	86.90	94.27	92.77

Citation

If you find our paper and repo useful, please cite our paper.

@article{liu2023improving,
  title={Improving Deep Representation Learning via Auxiliary Learnable Target Coding},
  author={Liu, Kangjun and Chen, Ke and Wang, Yaowei and Jia, Kui},
  journal={arXiv preprint arXiv:2305.18680},
  year={2023}
}

Contact

If you have any questions about our work, feel free to contact us through email (wikangj.liu@mail.scut.edu.cn) or Github issues.

Reference

Some code and training settings are borrowed from PyTorch implementation of DTRG.