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Showing 1 to 5 of 5 for “"StyleGAN"”.

  1. Predicting and Modifying Memorability of Images

    … first found a hyperplane in the latent space of StyleGAN to separate high and low memorable images. We then modified the image memorability (while maintaining the identity and other facial features such as age, emotion, etc.) by moving in the positive or negative direction of this hyperplane …

    uwo Repository record for Predicting and Modifying Memorability of Images (opens in a new tab)

  2. NoiseLearner: An Unsupervised, Content-agnostic Approach to Detect Deepfake Images

    … generative models in our evaluation, such as StyleGAN3 and probabilistic denoising diffusion models (DDPM). We observe that Noise- Learner performs well on multiple datasets, achieving 96% accuracy on both StyleGAN and StyleGAN2 datasets.

    vt Repository record for NoiseLearner: An Unsupervised, Content-agnostic Approach to Detect Deepfake Images (opens in a new tab)

  3. Dissection of Deep Neural Networks

    … associative memories in a Progressive GAN or StyleGAN, altering learned rules that govern the appearance of specific object parts in the model.

    mit Repository record for Dissection of Deep Neural Networks (opens in a new tab)

  4. Interpreting Deep Neural Networks and Beyond: Visualization, Learning Dynamics, and Disentanglement

    … new losses and network architectures based on StyleGAN [karras et al., 2019] for semi-supervised high-resolution disentanglement learning. Experimental results show that using very limited supervision significantly improves disentanglement quality and that the proposed method can generalize …

    rice Repository record for Interpreting Deep Neural Networks and Beyond: Visualization, Learning Dynamics, and Disentanglement (opens in a new tab)

  5. Using Deep Learning to Extract Multicellular Aggregation Features of Myxococcus xanthus

    … phenotypic analysis, integrating ResNet and StyleGAN2 into a Variational AutoEncoders (VAEs), and using Siamese architectures as the similarity metric. This pipeline transforms high-resolution microscopy data into low-dimensional phenotypic feature vectors. Human evaluations confirmed the …

    rice Repository record for Using Deep Learning to Extract Multicellular Aggregation Features of Myxococcus xanthus (opens in a new tab)