• Corpus ID: 232320707

Out-of-Distribution Detection of Melanoma using Normalizing Flows

@article{Valiuddin2021OutofDistributionDO,
  title={Out-of-Distribution Detection of Melanoma using Normalizing Flows},
  author={Mir Valiuddin and C. Viviers},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.12672}
}
Generative modelling has been a topic at the forefront of machine learning research for a substantial amount of time. With the recent success in the field of machine learning, especially in deep learning, there has been an increased interest in explainable and interpretable machine learning. The ability to model distributions and provide insight in the density estimation and exact data likelihood is an example of such a feature. Normalizing Flows (NFs), a relatively new research field of… 

References

SHOWING 1-10 OF 49 REFERENCES

Why Normalizing Flows Fail to Detect Out-of-Distribution Data

This work demonstrates that flows learn local pixel correlations and generic image-to-latent-space transformations which are not specific to the target image dataset, and shows that by modifying the architecture of flow coupling layers the authors can bias the flow towards learning the semantic structure of the target data, improving OOD detection.

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Flow++ is proposed, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks, and has begun to close the significant performance gap that has so far existed between autoregressive models and flow- based models.

Latent Normalizing Flows for Discrete Sequences

A VAE-based generative model is proposed which jointly learns a normalizing flow-based distribution in the latent space and a stochastic mapping to an observed discrete space in this setting, finding that it is crucial for the flow- based distribution to be highly multimodal.

NICE: Non-linear Independent Components Estimation

We propose a deep learning framework for modeling complex high-dimensional densities called Non-linear Independent Component Estimation (NICE). It is based on the idea that a good representation is

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

This work is the first to propose multi-frame video prediction with normalizing flows, which allows for direct optimization of the data likelihood, and produces high-quality stochastic predictions.

Glow: Generative Flow with Invertible 1x1 Convolutions

Glow, a simple type of generative flow using an invertible 1x1 convolution, is proposed, demonstrating that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images.

Density estimation using Real NVP

This work extends the space of probabilistic models using real-valued non-volume preserving (real NVP) transformations, a set of powerful invertible and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact sampling, exact inference of latent variables, and an interpretable latent space.

Stabilizing Training of Generative Adversarial Networks through Regularization

This work proposes a new regularization approach with low computational cost that yields a stable GAN training procedure and demonstrates the effectiveness of this regularizer accross several architectures trained on common benchmark image generation tasks.

FloWaveNet : A Generative Flow for Raw Audio

FloWaveNet is proposed, a flow-based generative model for raw audio synthesis that requires only a single-stage training procedure and a single maximum likelihood loss, without any additional auxiliary terms, and it is inherently parallel due to the characteristics of generative flow.

Noise Flow: Noise Modeling With Conditional Normalizing Flows

Noise Flow is introduced, a powerful and accurate noise model based on recent normalizing flow architectures that represents the first serious attempt to go beyond simple parametric models to one that leverages the power of deep learning and data-driven noise distributions.