Previous work in Neural Loss Function Search (NLFS) has shown a lack of correlation between smaller surrogate functions and large convolutional neural networks with massive regularization. We expand upon this research by revealing another disparity that exists, correlation between different types of image augmentation techniques. We show that different loss functions can perform well on certain image augmentation techniques, while performing poorly on others. We exploit this disparity by performing an evolutionary search on five types of image augmentation techniques in the hopes of finding image augmentation specific loss functions. The best loss functions from each evolution were then taken and transferred to WideResNet-28-10 on CIFAR-10 and CIFAR-100 across each of the five image augmentation techniques. The best from that were then taken and evaluated by fine-tuning EfficientNetV2Small on the CARS, Oxford-Flowers, and Caltech datasets across each of the five image augmentation techniques. Multiple loss functions were found that outperformed cross-entropy across multiple experiments. In the end, we found a single loss function, which we called the inverse bessel logarithm loss, that was able to outperform cross-entropy across the majority of experiments. Neural loss function search (NFLS) is the field of automated machine learning dedicated to finding loss functions better than cross entropy for machine learning and deep learning tasks. NLFS has been applied to object detection (Liu et al., 2021), image segmentation (Li et al., 2022), and person re-identification (Gu et al., 2022).

Retrofitting and thermographic survey (TS) companies in Scotland collaborate with social housing providers to tackle fuel poverty. They employ ground-level infrared (IR) camera-based-TSs (GIRTSs) for collecting thermal images to identi-fy the heat loss sources resulting from poor insulation. However, this identifica-tion process is labor-intensive and time-consuming, necessitating extensive data processing. To automate this, an AI-driven approach is necessary. Therefore, this study proposes a deep learning (DL)-based segmentation framework using the Mask Region Proposal Convolutional Neural Network (Mask RCNN) to validate its applicability to these thermal images. The objective of the framework is to au-tomatically identify, and crop heat loss sources caused by weak insulation, while also eliminating obstructive objects present in those images. By doing so, it min-imizes labor-intensive tasks and provides an automated, consistent, and reliable solution. To validate the proposed framework, approximately 2500 thermal imag-es were collected in collaboration with industrial TS partner. Then, 1800 repre-sentative images were carefully selected with the assistance of experts and anno-tated to highlight the target objects (TO) to form the final dataset. Subsequently, a transfer learning strategy was employed to train the dataset, progressively aug-menting the training data volume and fine-tuning the pre-trained baseline Mask RCNN. As a result, the final fine-tuned model achieved a mean average precision (mAP) score of 77.2% for segmenting the TO, demonstrating the significant po-tential of proposed framework in accurately quantifying energy loss in Scottish homes.

One of the best ways to reduce overfitting is to collect more (good-quality) data. However, collecting more data is not always feasible or can be very expensive. A related technique is data augmentation. Data augmentation involves generating new data records or features from existing data, expanding the dataset without collecting more data. It helps improve model generalization by creating variations of original input data and making it harder to memorize irrelevant information from training examples or features. Data augmentation is common for image and text data, but also exists for tabular data.

Deep learning has been achieving decent performance in computer vision requiring a large volume of images, however, collecting images is expensive and difficult in many scenarios. To alleviate this issue, many image augmentation algorithms have been proposed as effective and efficient strategies. Understanding current algorithms is essential to find suitable methods or develop novel techniques for given tasks. In this paper, we perform a comprehensive survey on image augmentation for deep learning with a novel informative taxonomy. To get the basic idea why we need image augmentation, we introduce the challenges in computer vision tasks and vicinity distribution.

Now, without waiting further, let's move on to the model building part. We will apply the augmentation techniques that are discussed in this article to generate images and then use those images to train the model. We will be working on the emergency vs non-emergency vehicle classification problem. You should be familiar with the problem statement if you've gone through my previous PyTorch articles. The aim of this project is to classify the images of vehicles as emergency or non-emergency. And you guessed it – it's an image classification problem.

Now, without waiting further, let's move on to the model building part. We will apply the augmentation techniques that are discussed in this article to generate images and then use those images to train the model. We will be working on the emergency vs non-emergency vehicle classification problem. You should be familiar with the problem statement if you've gone through my previous PyTorch articles. The aim of this project is to classify the images of vehicles as emergency or non-emergency. And you guessed it – it's an image classification problem.