The **Simple Neural Attention Meta-Learner**, or **SNAIL**, combines the benefits of temporal convolutions and attention to solve meta-learning tasks. They introduce positional dependence through temporal convolutions to make the model applicable to reinforcement tasks - where the observations, actions, and rewards are intrinsically sequential. They also introduce attention in order to provide pinpoint access over an infinitely large context. SNAIL is constructing by combining the two: we use temporal convolutions to produce the context over which we use a causal attention operation.

[Neural architecture search](https://paperswithcode.com/method/neural-architecture-search) (NAS) has witnessed prevailing success in image classification and (very recently) segmentation tasks. In this paper, we present the first preliminary study on introducing the NAS algorithm to generative adversarial networks (GANs), dubbed AutoGAN. The marriage of NAS and GANs faces its unique challenges. We define the search space for the generator architectural variations and use an RNN controller to guide the search, with parameter sharing and dynamic-resetting to accelerate the process. Inception score is adopted as the reward, and a multi-level search strategy is introduced to perform NAS in a progressive way.

AutoGAN

AutoGAN: Neural Architecture Search for Generative Adversarial Networks

SNAIL

A Simple Neural Attentive Meta-Learner

**Jigsaw** is a self-supervision approach that relies on jigsaw-like puzzles as the pretext task in order to learn image representations.

Source	A Simple Neural Attentive Meta-Learner
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com