Orthogonality and graph divergence losses promote disentanglement in generative modelsdoi:10.3389/fcomp.2024.1274779Shukla, AnkitaDadhich, RishiSingh, RajhansRayas, AnirudhSaidi, PouriaDasarathy, GautamBerisha, VisarTuraga, PavanFrontiers in Computer Science...
For cryoDRGN, due to the lack of validation metrics, the number of training epochs was chosen based on the quality of the density maps reconstructed. By contrast, OPUS-DSD selected models for further analyses according to the average reconstruction errors using the validation set. The structural ...
Machine Learning (ML) engages in the development of theories and algorithms for building computation models to make predictions or decisions based on sample data. ML has important empirical successes on data, such as images, signals, texts and speech, with outcomes akin to human cognition and disc...
Recent Monte Carlo denoising methods have achieved impressive progress in generating visually compelling images, which accelerate path-tracing based rendering. However, most of them rely on strong supervision, resulting in a high computational burden for creating paired data and potential overfitting issues...
2.1. GAN-Based Image Synthesis GAN, a framework for estimating generative models via an adversarial process, was introduced by Goodfellow et al. [2]. GANs comprise two components: a generator, which creates data samples from random noise, and a discriminator, which discerns whether these samples...
tandem mass spectrometry; deep learning; generative models; variational autoencoder; disentangled representation; latent space1. Introduction Unsupervised representation learning of metabolites based on tandem mass spectrometry (MS/MS) data has important implications for the identification and structural ...
and describe a technique based on data-driven generative models that achieves this goal. This enables us to ``re-render'' the sequences in ways that would not be possible with the input images alone. For example, we can stabilize a long sequence to focus on plant growth over many months...
GAN-based models [46,47] have shown good performance in improving the quality of generated images; however, they suffer from instability during training. Leveraging the superior training stability, approaches based on diffusion models [12] have recently emerged as a popular tool for generating near...