Learning to Transform for Generalizable Instance-wise Invariance

Computer vision research has long aimed to build systems that are robust to spatial transformations found in natural data. Traditionally, this is done using data augmentation or hard-coding invariances into the architecture. However, too much or too little invariance can hurt, and the correct amount is unknown a priori and dependent on the instance. Ideally, the appropriate invariance would be learned from data and inferred at test-time. We treat invariance as a prediction problem. Given any image, we use a normalizing flow to predict a distribution over transformations and average the predictions over them. Since this distribution only depends on the instance, we can align instances before classifying them and generalize invariance across classes. The same distribution can also be used to adapt to out-of-distribution poses. This normalizing flow is trained end-to-end and can learn a much larger range of transformations than Augerino and InstaAug. When used as data augmentation, our method shows accuracy and robustness gains on CIFAR 10, CIFAR10-LT, and TinyImageNet.Comment: Accepted to ICCV 202

Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models

Multi-spectral imagery is invaluable for remote sensing due to different spectral signatures exhibited by materials that often appear identical in greyscale and RGB imagery. Paired with modern deep learning methods, this modality has great potential utility in a variety of remote sensing applications, such as humanitarian assistance and disaster recovery efforts. State-of-the-art deep learning methods have greatly benefited from large-scale annotations like in ImageNet, but existing MSI image datasets lack annotations at a similar scale. As an alternative to transfer learning on such data with few annotations, we apply complex-valued co-domain symmetric models to classify real-valued MSI images. Our experiments on 8-band xView data show that our ultra-lean model trained on xView from scratch without data augmentations can outperform ResNet with data augmentation and modified transfer learning on xView. Our work is the first to demonstrate the value of complex-valued deep learning on real-valued MSI data.Comment: NeuRIPS 2022 HADR workshop submissio

Fractography analysis into low-C steel undergone through various destructive mechanical tests

The present work deals with a critical fractographic analysis into low carbon (0.18%-C) steel samples which were used for three different mechanical tests: tensile test; shear test; and toughness test. These mechanical tests were performed in standard sized specimens as recommended by ASTM. In each category of test, there were two different specimens with different physical states according to heat treated conditions. First specimen was in ‘as received’ condition and another was annealed. For annealing, sample was first heated up to austenitic temperature and inserted inside the sand for slow rate of cooling. As these two categories of samples were undergone through destructive tests, the variation in fracture behaviour of the samples was analysed by FESEM, XRD. A significant variation in fractographic images could be observed in different heat-treated samples. Micro-pores, dimples, cleavage facet, peaks, valleys, and cave formation were observed in the samples

Analysing strength, hardness and grain-structure of 0.2%-C steel specimens processed through an identical heating period with different continuous transformation rates

The present work deals with improvement of mechanical properties and refining the microstructure of low carbon steel (0.2%-C) after applying heat treatment techniques. For the purpose, five different samples were taken under study. First sample was kept in ‘as received’ condition and other four samples were undergone into heating process in an Induction furnace. The holding temperature of all the four samples were kept common i.e., 850 °C for a fixed period of 2.5 h. Then, these four samples were cooled into four different cooling media i.e., Air, Water, Oil, and Furnace. All the samples were in the form of rods with 195 mm length and 32 mm diameter. The universal testing machine was used to determine the tensile strength of all the samples. Rockwell hardness tester was used to find the hardness of samples. The microstructural variation was analysed through an optical microscope. All the results were analysed and compared with ‘as received’ sample. The Oil cooled sample showed the highest tensile strength of 585 MPa. The microstructural orientation of oil cooled sample i.e., bainite + fine lamella of ferrite and cementite, provides a good hardness, strength, and toughness to the steel. In addition, XRD and fractography analysis of the samples were also carried out