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**Soft Actor Critic (Autotuned Temperature** is a modification of the [SAC](https://paperswithcode.com/method/soft-actor-critic) reinforcement learning algorithm. [SAC](https://paperswithcode.com/method/sac) can suffer from brittleness to the temperature hyperparameter. Unlike in conventional reinforcement learning, where the optimal policy is independent of scaling of the reward function, in maximum entropy reinforcement learning the scaling factor has to be compensated by the choice a of suitable temperature, and a sub-optimal temperature can drastically degrade performance. To resolve this issue, SAC with Autotuned Temperature has an automatic gradient-based temperature tuning method that adjusts the expected entropy over the visited states to match a target value.

Soft Actor-Critic (Autotuned Temperature)

Soft Actor-Critic Algorithms and Applications

Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation

**Hierarchical Style Disentanglement**, or **HiSD**,  aims to disentangle different styles in image-to-image translation models. It organizes the labels into a hierarchical structure, where independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. To make the styles identified to the tags and attributes, the authors carefully redesign the modules, phases, and objectives.

Source	Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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What is: Surrogate Lagrangian Relaxation?

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