**Informative Sample Mining Network** is a multi-stage sample training scheme for GANs to reduce sample hardness while preserving sample informativeness. Adversarial Importance Weighting is proposed to select informative samples and assign them greater weight. The authors also propose Multi-hop Sample Training to avoid the potential problems in model training caused by sample mining. Based on the principle of divide-and-conquer, the authors produce target images by multiple hops, which means the image translation is decomposed into several separated steps.

**Spectral-Normalized Identity Priors**, or **SNIP**, is a structured pruning approach that penalizes an entire [residual module](https://paperswithcode.com/method/residual-connection) in a [Transformer model](https://paperswithcode.com/method/residual-connection) toward an identity mapping. It is applicable to any structured module, including a single [attention head](https://paperswithcode.com/method/scaled), an [entire attention block](https://paperswithcode.com/method/multi-head-attention), or a [feed-forward subnetwork](https://paperswithcode.com/method/position-wise-feed-forward-layer). The method identifies and discards unimportant non-linear mappings in the [residual connections](https://paperswithcode.com/method/residual-connection) by applying a thresholding operator on the function norm. Furthermore, [spectral normalization](https://paperswithcode.com/method/spectral-normalization) to stabilize the distribution of the post-activation values of the [Transformer](https://paperswithcode.com/method/transformer) layers, further improving the pruning effectiveness of the proposed methodology.

Spectral-Normalized Identity Priors

Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior

Informative Sample Mining Network

Informative Sample Mining Network for Multi-Domain Image-to-Image Translation

**NoisyNet-A3C** is a modification of [A3C](https://paperswithcode.com/method/a3c) that utilises noisy linear layers for exploration instead of 
$\epsilon$-greedy exploration as in the original [DQN](https://paperswithcode.com/method/dqn) formulation.

Source	Informative Sample Mining Network for Multi-Domain Image-to-Image Translation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com