Background Invariance by Adversarial Learning

Companion code to the publication:

• Background Invariance by Adversarial Learning by Ricardo Cruz, Ricardo M. Prates, Eduardo F. Simas Filho, Joaquim F. Pinto Costa and Jaime S. Cardoso. ICPR 2020. (to appear)

Abstract: Convolutional neural networks are shown to be vulnerable to changes in the background. The proposed method is an end-to-end method that augments the training set by introducing new backgrounds during the training process. These backgrounds are created by a generative network that is trained as an adversary to the model. A case study is explored based on overhead power line insulators detection using a drone -- a training set is prepared from photographs taken inside a laboratory and then evaluated using photographs that are harder to collect from outside the laboratory. The proposed method improves performance by over 20% for this case study.

The idea in a nutshell:

Table: Background changes can produce wild disparate accuracies (%)

Figure: Proposed adversarial background augmentation during training.

The model (e.g. classifier, regressor, whatever) tries to minimize a loss and a background generator injects fake backgrounds into the images in order to maximize the loss. In order to inject the backgrounds, we need either manual segmentations or to use a mask generator which is trained in the process. Notice this is not a GAN: there is no discriminator, the backgrounds are not meant to be realistic.

Code organization:

In the code, we focused on classification, but the framework could be used for other tasks. The code uses TensorFlow 2.x.

1. train1.py, train2.py, train3.py: these are the training files. For better control over the optimization process and debugging, we decided to divide the optimization process in three phases. Phase 1: train only the classifier. Phase 2: train the mask generator. Phase 3: train both the classifier and background generator adversarially.
2. train_att.py: this is a model from the literature we used to contrast our work against (implemented by ourselves). See: Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification (2018).
3. mybackgrounds.py, mydatagen.py, mydatasets.py: auxiliary files that automatically download the datasets (not all used in the paper) and, in soma cases, create testing versions with new backgrounds.
4. mymodels.py: auxiliary file with the architectures.
5. evaluate.py and evaluate_seg.py: simple evaluation procedures.

Contact: Ricardo Cruz, [email protected], http://rpmcruz.github.io.