**OverFeat** is a classic type of convolutional neural network architecture, employing [convolution](https://paperswithcode.com/method/convolution), pooling and fully connected layers. The Figure to the right shows the architectural details.

**Inverse Q-Learning (IQ-Learn)** is a a simple, stable & data-efficient framework for Imitation Learning (IL), that directly learns *soft Q-functions* from expert data. IQ-Learn enables **non-adverserial** imitation learning, working on both offline and online IL settings. It is performant even with very sparse expert data, and scales to complex image-based environments, surpassing prior methods by more than **3x**. 

It is very simple to implement requiring ~15 lines of code on top of existing RL methods.

<span class="description-source">Source: [IQ-Learn: Inverse soft Q-Learning for Imitation](https://arxiv.org/abs/2106.12142)</span>

IQ-Learn

OverFeat

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

**Subformer** is a [Transformer](https://paperswithcode.com/method/transformer) that combines sandwich-style parameter sharing, which overcomes naive cross-layer parameter sharing in generative models, and self-attentive embedding factorization (SAFE). In SAFE, a small self-attention layer is used to reduce embedding parameter count.

Source	OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com