The input image was cropped into a fix size of 512 512 using zero padding if needed.

Boosting is a widely used machine learning approach based on the idea of aggregating weak learning rules.

Many machine learning models involve mapping a score vector to a probability vector. Usually, this is done by projecting the score vector onto a probability simplex, and such projections are often characterized as Lipschitz continuous approximations of the argmax function, whose Lipschitz constant is controlled by a parameter that is similar to a softmax temperature.

We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data.

Here we provide conceptual illustrations of our stochastic annealing idea on a simple example. As mentioned in Section 3.3 and 4, lossy bits-back modifies the above Base Hyperprior as follows: All methods were tuned on a best-effort basis to ensure convergence, except that STE consistently encountered convergence issues even with a tiny learning rate (see [Yin et al., 2019]). The rate-distortion results for MAP and STE were calculated with early stopping (i.e., using the intermediate Figures in the bottom row focus on the same cropped region of images in the top row. RGB; higher values are better.

Habibian et al., 2019, Y ang et al., 2020a], which can reduce a sizable amount of global internet traffic. State-of-the-art neural methods for lossy image compression [Ballé et al., 2018, Minnen et al., 2018, Lee et al., 2019] learn a mapping between images and latent variables with a variational

Long-term visual object tracking benchmark.