Interactive Latent Interpolation on MNIST Dataset
원문 초록
This paper will discuss the potential of dimensionality reduction with a web-based use of GANs. Throughout a variety of experiments, we show synthesizing visually-appealing samples, interpolating meaningfully between samples, and performing linear arithmetic with latent vectors. GANs have proved to be a remarkable technique to produce computer-generated images, very similar to an original image. This is primarily useful when coupled with dimensionality reduction as an effective application of our algorithm. We proposed a new architecture for GANs, which ended up not working for mathematical reasons later explained. We then proposed a new web-based GAN that still takes advantage of dimensionality reduction to speed generation in the browser to .2 milliseconds. Lastly, we made a modern UI with linear interpolation to present the work. With the speedy generation, we can generate so fast that we can create an animation type effect that we have never seen before that works on both web and mobile.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Active Learning for Manifold Gaussian Process Regression
Distributed Lyapunov Functions for Nonlinear Networks
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction
Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reduction
Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.