Combines the channel attention of the widely known [spatial squeeze and channel excitation (SE)](https://paperswithcode.com/method/squeeze-and-excitation-block) block and the spatial attention of the [channel squeeze and spatial excitation (sSE)](https://paperswithcode.com/method/channel-squeeze-and-spatial-excitation#) block to build a spatial and channel attention mechanism for image segmentation tasks.

**Target Policy Smoothing** is a regularization strategy for the value function in reinforcement learning. Deterministic policies can overfit to narrow peaks in the value estimate, making them highly susceptible to functional approximation error, increasing the variance of the target. To reduce this variance, target policy smoothing adds a small amount of random noise to the target policy and averages over mini-batches - approximating a [SARSA](https://paperswithcode.com/method/sarsa)-like expectation/integral.

The modified target update is:

$$ y = r + \gamma{Q}\_{\theta'}\left(s', \pi\_{\theta'}\left(s'\right) + \epsilon \right) $$

$$ \epsilon \sim \text{clip}\left(\mathcal{N}\left(0, \sigma\right), -c, c \right) $$

where the added noise is clipped to keep the target close to the original action. The outcome is an algorithm reminiscent of [Expected SARSA](https://paperswithcode.com/method/expected-sarsa), where the value estimate is instead learned off-policy and the noise added to the target policy is chosen independently of the exploration policy. The value estimate learned is with respect to a noisy policy defined by the parameter $\sigma$.

Target Policy Smoothing

Addressing Function Approximation Error in Actor-Critic Methods

Concurrent Spatial and Channel Squeeze & Excitation

Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks

**Sparse Sinkhorn Attention** is an attention mechanism that reduces the memory complexity of the [dot-product attention mechanism](https://paperswithcode.com/method/scaled) and is capable of learning sparse attention outputs. It is based on the idea of differentiable sorting of internal representations within the self-attention module. SSA incorporates a meta sorting network that learns to rearrange and sort input sequences. Sinkhorn normalization is used to normalize the rows and columns of the sorting matrix. The actual SSA attention mechanism then acts on the block sorted sequences.

Source	Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com