**GENets**, or **GPU-Efficient Networks**, are a family of efficient models found through [neural architecture search](https://paperswithcode.com/methods/category/neural-architecture-search). The search occurs over several types of convolutional block, which include [depth-wise convolutions](https://paperswithcode.com/method/depthwise-convolution), [batch normalization](https://paperswithcode.com/method/batch-normalization), [ReLU](https://paperswithcode.com/method/relu), and an [inverted bottleneck](https://paperswithcode.com/method/inverted-residual-block) structure.

**Sticker Response Selector**, or **SRS**, is a model for multi-turn dialog that automatically selects a sticker response. SRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker with each utterance in the dialog history. SRS then learns the short-term and long-term dependency between all interaction results by a fusion network to output the the final matching score.

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

GENet

Neural Architecture Design for GPU-Efficient Networks

**Meta Face Recognition** (MFR) is a meta-learning face recognition method. MFR synthesizes the source/target domain shift with a meta-optimization objective, which requires the model to learn effective representations not only on synthesized source domains but also on synthesized target domains. Specifically, domain-shift batches are built through a domain-level sampling strategy and back-propagated gradients/meta-gradients are obtained on synthesized source/target domains by optimizing multi-domain distributions. The gradients and meta-gradients are further combined to update the model to improve generalization.

Source	Neural Architecture Design for GPU-Efficient Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com