A Survey of Lottery Ticket Hypothesis

The Lottery Ticket Hypothesis (LTH) states that a dense neural network model contains a highly sparse subnetwork (i.e., winning tickets) that can achieve even better performance than the original model when trained in isolation. While LTH has been proved both empirically and theoretically in many works, there still are some open issues, such as efficiency and scalability, to be addressed. Also, the lack of open-source frameworks and consensual experimental setting poses a challenge to future research on LTH. We, for the first time, examine previous research and studies on LTH from different perspectives. We also discuss issues in existing works and list potential directions for further exploration. This survey aims to provide an in-depth look at the state of LTH and develop a duly maintained platform to conduct experiments and compare with the most updated baselines.

Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors

Purpose: Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia. Clinicians routinely measure nodal size to monitor disease progression, confirm metastatic cancer, and assess treatment response. However, variations in their shapes and appearances make it cumbersome to identify LNs, which reside outside of most organs. Methods: We propose to segment LNs in the mediastinum by leveraging the anatomical priors of 28 different structures (e.g., lung, trachea etc.) generated by the public TotalSegmentator tool. The CT volumes from 89 patients available in the public NIH CT Lymph Node dataset were used to train three 3D nnUNet models to segment LNs. The public St. Olavs dataset containing 15 patients (out-of-training-distribution) was used to evaluate the segmentation performance. Results: For the 15 test patients, the 3D cascade nnUNet model obtained the highest Dice score of 72.2 +- 22.3 for mediastinal LNs with short axis diameter $\geq$ 8mm and 54.8 +- 23.8 for all LNs respectively. These results represent an improvement of 10 points over a current approach that was evaluated on the same test dataset. Conclusion: To our knowledge, we are the first to harness 28 distinct anatomical priors to segment mediastinal LNs, and our work can be extended to other nodal zones in the body. The proposed method has immense potential for improved patient outcomes through the identification of enlarged nodes in initial staging CT scans.

Successfully Applying Lottery Ticket Hypothesis to Diffusion Model

Despite the success of diffusion models, the training and inference of diffusion models are notoriously expensive due to the long chain of the reverse process. In parallel, the Lottery Ticket Hypothesis (LTH) claims that there exists winning tickets (i.e., aproperly pruned sub-network together with original weight initialization) that can achieve performance competitive to the original dense neural network when trained in isolation. In this work, we for the first time apply LTH to diffusion models. We empirically find subnetworks at sparsity 90%-99% without compromising performance for denoising diffusion probabilistic models on benchmarks (CIFAR-10, CIFAR-100, MNIST). Moreover, existing LTH works identify the subnetworks with a unified sparsity along different layers. We observe that the similarity between two winning tickets of a model varies from block to block. Specifically, the upstream layers from two winning tickets for a model tend to be more similar than the downstream layers. Therefore, we propose to find the winning ticket with varying sparsity along different layers in the model. Experimental results demonstrate that our method can find sparser sub-models that require less memory for storage and reduce the necessary number of FLOPs. Codes are available at https://github.com/osier0524/Lottery-Ticket-to-DDPM.

UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video

We show how to build a model that allows realistic, free-viewpoint renderings of a scene under novel lighting conditions from video. Our method -- UrbanIR: Urban Scene Inverse Rendering -- computes an inverse graphics representation from the video. UrbanIR jointly infers shape, albedo, visibility, and sun and sky illumination from a single video of unbounded outdoor scenes with unknown lighting. UrbanIR uses videos from cameras mounted on cars (in contrast to many views of the same points in typical NeRF-style estimation). As a result, standard methods produce poor geometry estimates (for example, roofs), and there are numerous ''floaters''. Errors in inverse graphics inference can result in strong rendering artifacts. UrbanIR uses novel losses to control these and other sources of error. UrbanIR uses a novel loss to make very good estimates of shadow volumes in the original scene. The resulting representations facilitate controllable editing, delivering photorealistic free-viewpoint renderings of relit scenes and inserted objects. Qualitative evaluation demonstrates strong improvements over the state-of-the-art.

Random Subspace Learning Approach to High-Dimensional Outliers Detection

We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like minimum covariance determinant (MCD) by computing the needed determinants and associated measures in much lower dimensional subspaces. Both theoretical and computational development of our approach reveal that it is computationally more efficient than the regularized methods in high-dimensional low-sample size, and often competes favorably with existing methods as far as the percentage of correct outlier detection is concerned.