FashionCLIP is a fine-tuned CLIP model on fashion data (more than 800K pairs). It is the first foundation model for Fashion.

**Population Based Augmentation**, or **PBA**, is a data augmentation strategy (PBA), which generates nonstationary augmentation policy schedules instead of a fixed augmentation policy. In PBA we consider the augmentation policy search problem as a special case of hyperparameter schedule learning. It leverages [Population Based Training](https://paperswithcode.com/method/population-based-training) (PBT), a hyperparameter search algorithm which
optimizes the parameters of a network jointly with their hyperparameters to maximize performance. The output of PBT is not an optimal hyperparameter configuration but rather a trained model and schedule of hyperparameters. 

In PBA, we are only interested in the learned schedule and discard the child model result (similar to [AutoAugment](https://paperswithcode.com/method/autoaugment)). This learned augmentation schedule can then be used to improve the training of different (i.e., larger and costlier to train) models on the same dataset.

PBT executes as follows. To start, a fixed population of models are randomly initialized and trained in parallel. At certain intervals, an “exploit-and-explore” procedure is applied to the worse performing population members, where the model clones the weights of a better performing model (i.e., exploitation) and then perturbs the hyperparameters of the cloned model to search in the hyperparameter space (i.e., exploration). Because the weights of the models are cloned and never reinitialized, the total computation required is the computation to train a single model times the population size.

Population Based Augmentation

Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules

FashionCLIP

Contrastive language and vision learning of general fashion concepts

**NVAE**, or **Nouveau VAE**, is deep, hierarchical variational autoencoder. It can be trained with the original [VAE](https://paperswithcode.com/method/vae) objective, unlike alternatives such as [VQ-VAE-2](https://paperswithcode.com/method/vq-vae-2). NVAE’s design focuses on tackling two main challenges: (i) designing expressive neural
networks specifically for VAEs, and (ii) scaling up the training to a large number of hierarchical
groups and image sizes while maintaining training stability.

To tackle long-range correlations in the data, the model employs hierarchical multi-scale modelling. The generative model starts from a small spatially arranged latent variables as $\mathbf{z}\_{1}$ and samples from the hierarchy group-by-group while gradually doubling the spatial dimensions. This multi-scale approach enables NVAE to capture global long-range correlations at the top of the hierarchy and local fine-grained dependencies at the lower groups.

Additional design choices include the use of residual cells for the generative models and the encoder, which employ a number of tricks and modules to achieve good performance, and the use of residual normal distributions to smooth optimization. See the components section for more details.

Source	Contrastive language and vision learning of general fashion concepts
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com