**Inception-ResNet-v2-B** is an image model block for a 17 x 17 grid used in the [Inception-ResNet-v2](https://paperswithcode.com/method/inception-resnet-v2) architecture. It largely follows the idea of Inception modules - and grouped convolutions - but also includes residual connections.

**Domain Adaptive Neighborhood Clustering via Entropy Optimization (DANCE)** is a self-supervised clustering method that harnesses the cluster structure of the target domain using self-supervision. This is done with a neighborhood clustering technique that self-supervises feature learning in the target. At the same time, useful source features and class boundaries are preserved and adapted with a partial domain alignment loss that the authors refer to as entropy separation loss. This loss allows the model to either match each target example with the source, or reject it as unknown.

DANCE

Universal Domain Adaptation through Self Supervision

Inception-ResNet-v2-B

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

**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 $\sigma$.

Source	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com