Simple and Robust Loss Design for Multi-Label Learning with Missing Labels

Official PyTorch Implementation of the paper Simple and Robust Loss Design for Multi-Label Learning with Missing Labels

Youcai Zhang, Yuhao Cheng, Xinyu Huang, Fei Wen, Rui Feng, Yaqian Li, Yandong Guo

OPPO Research Institute, Shanghai Jiao Tong University, Fudan University


Multi-label learning in the presence of missing labels(MLML) is a challenging problem. Existing methods mainly focus on the design of network structures or training schemes, which increase the complexity of implementation. This work seeks to fulfill the potential of loss function in MLML without increasing the procedure and complexity. Toward this end, we propose two simple yet effective methods via robust loss design based on an observation that a model can identify missing labels during training with a high precision. The first is a novel robust loss for negatives, namely the Hill loss, which re-weights negatives in the shape of a hill to alleviate the effect of false negatives. The second is a self-paced loss correction (SPLC) method, which uses a loss derived from the maximum likelihood criterion under an approximate distribution of missing labels. Comprehensive experiments on a vast range of multi-label image classification datasets demonstrate that our methods can remarkably boost the performance of MLML and achieve new state-of-the-art loss functions in MLML.

Credit to previous work

This repository is built upon the code base of ASL, thanks very much!


We construct the training sets of missing labels by randomly dropping positive labels of each training image with different ratios.

samples classes Labels avg. label/img File
COCO-full labels 82,081 80 241,035 2.9 coco_train_full.txt
COCO-75% labels left 82,081 80 181,422 2.2 coco_train_0.75left.txt
COCO-40% labels left 82,081 80 96,251 1.2 coco_train_0.4left.txt
COCO-single label 82,081 80 82,081 1.0 coco_train_singlelabel.txt

Loss Implementation

In this PyTorch file, we provide
implementations of our loss functions: Hill and SPLC. The loss functions take logits (predicted logits before sigmoid) and targets as input, and return the loss. Note that SPLC also takes current training epoch as input.

  • class Hill(nn.Module)
  • class SPLC(nn.Module)

Training Code

Training model by selecting different losses:

python --loss Hill --data {path to dataset} --dataset {select training dataset}
python --loss SPLC --data {path to dataset} --dataset {select training dataset}

For example:

python --loss Hill --data '/home/MSCOCO_2014/' --dataset './dataset/coco_train_0.4left.txt'

Validation Code

We provide validation code that reproduces results reported in the paper on MS-COCO:

python --model_path {path to model to validate} --data {path to dataset}


        title={Simple and Robust Loss Design for Multi-Label Learning with Missing Labels}, 
        author={Youcai Zhang and Yuhao Cheng and Xinyu Huang and Fei Wen and Rui Feng and Yaqian Li and Yandong Guo},


View Github