Pruning algorithms compress deep neural networks while incurring in minimal degradation of accuracy. We propose Stochastic Pruning: an algorithm in which dense solutions are perturbed with Gaussian noise prior to pruning. We use Stochastic Pruning to explore the basin of attraction around one solution, obtained with a single minimiser. Using ResNet18, ResNet50, and VGG19 on CIFAR-10 and CIFA R-100, we show that SP consistently improves one-shot accuracy after extreme pruning and mitigates feature variance explosion. Our analysis adds understanding by linking one-shot accuracy and local gradients.We show that fine-tuned SP accuracy can outperform deterministically pruned solutions in most cases. Finally, we leverage multi-objective evolutionary optimisation to compare single and two-objective results, e.g. optimised for both accuracy and sparsity. The good performance of SP provides a promising approach to extreme pruning, achieving viable high accuracy, and high efficiency neural networks in resource-constrained scenarios.

3D shape reconstruction typically requires identifying object features or textures in multiple images of a subject. This approach is not viable when the subject is semi-transparent and moving in and out of focus. Here we overcome these challenges by rendering a candidate shape with adaptive blurring and transparency for comparison with the images. We use the microscopic nematode Caenorhabditis elegans as a case study as it freely explores a 3D complex fluid with constantly changing optical properties. We model the slender worm as a 3D curve using an intrinsic parametrisation that naturally admits biologically-informed constraints and regularisation. To account for the changing optics we develop a novel differentiable renderer to construct images from 2D projections and compare against raw images to generate a pixel-wise error to jointly update the curve, camera and renderer parameters using gradient descent. The method is robust to interference such as bubbles and dirt trapped in the fluid, stays consistent through complex sequences of postures, recovers reliable estimates from blurry images and provides a significant improvement on previous attempts to track C. elegans in 3D. Our results demonstrate the potential of direct approaches to shape estimation in complex physical environments in the absence of ground-truth data.