arXiv subjects

Su-Jin Shin

Publications and source records attributed to Su-Jin Shin.

3 recordsLinked to original sources

arXiv2026

Programmable spatial coherence tomography: diffraction-limited three-dimensional reflection imaging under modulated monochromatic illumination

Depth sectioning in reflection microscopy has predominantly relied on temporal coherence gating. Here we show that volumetric reflection tomography at diffraction-limited resolution can be achieved under monochromatic illumination by engineering spatial, rather than temporal, coherence. In programmable spatial coherence tomography (PSCT), a sequence of pupil-coded illumination patterns with angular-spectrum diversity generates measurement redundancy enabling the system to calibrate itself, jointly retrieving aberrations, illumination profiles, and sample motion without guide stars or modal priors. We demonstrate label-free volumetric imaging of thick human tissues, organoids, frequency-resolved dynamic contrast, and high-resolution in vivo brain imaging through a cranial window. These results position PSCT as an alternative to temporal coherence based reflection imaging in complex biological systems.

physics.optics↗

arXiv2019

Hierarchically Clustered Representation Learning

The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which often involves cohesive clustering with a focus on instance relations. To overcome the limitations of flat clustering, we introduce hierarchically-clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. Compared with a few prior works, HCRL firstly attempts to consider a generation of deep embeddings from every component of the hierarchy, not just leaf components. In addition to obtaining hierarchically clustered embeddings, we can reconstruct data by the various abstraction levels, infer the intrinsic hierarchical structure, and learn the level-proportion features. We conducted evaluations with image and text domains, and our quantitative analyses showed competent likelihoods and the best accuracies compared with the baselines.

cs.LG↗

arXiv2017

Adversarial Dropout for Supervised and Semi-supervised Learning

Recently, the training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has been proved to improve generalization performance of neural networks. In contrast to the individually biased inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts that maximize the divergence between the outputs from the network with the dropouts and the training supervisions. The identified adversarial dropout are used to reconfigure the neural network to train, and we demonstrated that training on the reconfigured sub-network improves the generalization performance of supervised and semi-supervised learning tasks on MNIST and CIFAR-10. We analyzed the trained model to reason the performance improvement, and we found that adversarial dropout increases the sparsity of neural networks more than the standard dropout does.

cs.LG↗