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Title: Is my Deep Learning Model Learning more than I want it to?

Paper content:
Existing deep learning approaches for learning visual features tend to extract more information than what is required for the task at hand.
From a privacy preservation perspective, the input visual information is not protected from the model; enabling the model to become more intelligent than it is trained to be.
Existing approaches for suppressing additional task learning assume the presence of ground truth labels for the tasks to be suppressed during training time.
In this research, we propose a three-fold novel contribution:
(i) a novel metric to measure the trust score of a trained deep learning model,
(ii) a model-agnostic solution framework for trust score improvement by suppressing all the unwanted tasks, and
(iii) a simulated benchmark dataset, PreserveTask, having five different fundamental image classification tasks to study the generalization nature of models.
In the first set of experiments, we measure and improve the trust scores of five popular deep learning models: VGG16, VGG19, Inception-v1, MobileNet, and DenseNet and demonstrate that Inception-v1 is having the lowest trust score.
Additionally, we show results of our framework on color-MNIST dataset and practical applications of face attribute preservation in Diversity in Faces (DiF) and IMDB-Wiki dataset.
The primary objective of artificial intelligence is to imitate human intelligence tabular rasa.
Especially, with the advent of deep learning (DL), the models are striving to perform composite tasks by learning complex relationships and patterns available in noisy, unstructured data (Ruder, 2017) .
With this sudden growth in the consumption of data by models, there has been a lot of study on the privacy and security of the learnt model (Shokri & Shmatikov, 2015) .
Data governance and model governance frameworks, control and protect sharing of data and model meta information between two entities and also their social implications (Helbing, 2019) .
The premise of model privacy has majorly revolved around preserving the model content from human (man-in-the-middle) adversarial attacks (Abadi et al., 2016) .
However, the model itself could learn all the private information from the data and become much more intelligent than the original intent it was trained for.
With the strive for model generalization, including techniques for transfer learning and multi-task learning, the model is encouraged to learn more and more generic features from the data that could be used for more than one task (Søgaard & Goldberg, 2016) .
Consider the example described in Figure 1 , where a classifier is trained to detect the shape of an object from images.
However, using the features extracted by the above classifier, the size and location of the object in the image can also be predicted.
Thus, a shape classifier is more intelligent than its objective of only predicting the shape of the object.
While in certain applications, this is a required property of classification models (such as in, transfer learning and domain adaptation), in most of the privacy preserving applications, the data and its other visual attributes have to be kept private from the model itself.
As an additional real-world example, we train a DL model to predict the gender from a face image.
However, the DL model learns most generic features from the face image, enabling it to predict the age and the identity of the person.
The input face image could be saved securely from a human attacker, however, there is not much focus on securing from the model itself.
Additionally as shown in Figure 1
(a), the task of debiasing is to remove the the bias (color) in learning a specific task (shape).
This happens due to the high correlation between the color and shapes in the input images.
However, as shown in Figure 1
(b), our task in model trust is to forcefully The fundamental research motivation in this work is to study if a learning model could be restricted to perform only one or a specific group of tasks.
ensure that the model learns to perform only one or few selected tasks (shape) from the input images and unlearn all other tasks (color, size, location).
If multi-class classification tasks could be done from the same image, the research question is, "How can we ensure that the model is learnt only for one or a few tasks (called as, preserved tasks), and is strictly not learnt for the other tasks (called as, suppressed tasks)?".
To pursue research on this problem, there are few evident challenges:
(i) there is a lack of a balanced and properly curated image dataset where multiple classification tasks could be performed on the same image,
(ii) the complete knowledge of both the preserved tasks and the suppressed tasks should be known apriori, that is, we cannot suppress those tasks that we don't have information about, and
(iii) presence of very few model agnostic studies to preserve and suppress different task groups.
In this research, we propose a novel framework to measure the trust score of a trained DL model and a solution approach to improve the trust score during training.
The major research contributions are summarized as follows:
1. A simulated, class-balanced, multi-task dataset, PreserveTask with five tasks that could be performed on each image: shape, size, color, location, and background color classification.
2. A novel metric to measure the trustworthiness score of a trained DL model.
The trust scores of five popular DL models are measured and compared: VGG16, VGG19, Inception-v1, MobileNet, and DenseNet.
A generic model-agnostic solution framework to improve the trust scores of DL models during training by preserving a few tasks and suppressing other tasks on the same image.
3. Experimental analysis are performed for the proposed framework in comparison with other existing approaches under different settings.
Experimentally, we considered the model with the least trust score, Inception-v1, and showed that the proposed framework aids in improving the overall trust score 1 .
4. To demonstrate the practical applications and generalizability of the metric and the solution framework, we show additionally results in colored MNIST dataset and face attribute preservation using two datasets:
(i) Diversity in Faces (DiF) (Merler et al.)
(ii) IMDBWiki (Rothe et al., 2018).
In this research, we showcased a model-agnostic framework for measuring and improving the trustworthiness of a model from a privacy preservation perspective.
The proposed framework did not assume the need for the suppression task labels during train time, while, similar performance could be obtained by training using random classification boundaries.
A novel simulated benchmark dataset called PreserveTask was created to methodically evaluate and analyze a DL model's capability in suppressing shared task learning.
This dataset opens up further research opportunities in this important and practically necessary research domain.
Experimentally, it was shown that popular DL models such as VGG16, VGG19, Inception-v1, DenseNet, and MobileNet show poor trust scores and tend to be more intelligent than they were trained for.
Also, we show a practical case study of our proposed approach in face attribute classification using:
(i) Diversity in Faces (DiF) and
(ii) IMDB-Wiki datasets.
We would like to extend this work by studying the effect of multi-label classification tasks during suppression.

assistant: text: Can we trust our deep learning models? A framework to measure and improve a deep learning model's trust during training.