Can deep learning techniques improve the similarity of output in generative models?
Best insight from top research papers
Deep learning techniques can improve the similarity of output in generative models. For example, one paper proposes a novel idea to generate disentangled representations using a Convolutional Encoder and a Label-Scaler network . Another paper incorporates symmetries into generative models and deep architectures to achieve enhanced generalization and guaranteed performance under input transformations . Additionally, a paper introduces a method called group-based subset scanning to identify and characterize creative processes in generative models, which can lead to more compelling and human-like outputs . These papers demonstrate how deep learning techniques can enhance the similarity and creativity of output in generative models.
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| Papers (5) | Insight |
|---|---|
| The provided paper does not specifically discuss generative models or the improvement of similarity in their output. The paper focuses on cross-linguistic similarity evaluation techniques based on deep learning. | |
01 Mar 2022 | The provided paper does not directly address the question of improving the similarity of output in generative models. The paper focuses on detecting and characterizing creative processes and artifacts in generative models using group-based subset scanning methods. |
| The paper discusses incorporating symmetries into generative models to improve generalization and performance under input transformations, but it does not specifically mention improving the similarity of output in generative models. | |
01 Jan 2023 | The paper does not directly address the question of improving the similarity of output in generative models. The paper focuses on disentangling different factors of variations in generated images and increasing the disentanglement using a novel approach. |
5 Citations | The paper discusses an extension to a deep generative method that improves the similarity of output molecules by replacing parts with fragments of similar 3D shape and electrostatics. |
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