Category: End-to-End

End-to-End

Nix-TTS: An Incredibly Lightweight End-to-End Text-to-Speech Model via Non End-to-End Distillation

An Incredibly Lightweight End-to-End Text-to-Speech Model via Non End-to-End Distillation Rendi Chevi, Radityo Eko Prasojo, Alham Fikri Aji This is a repository for our paper, 🐤 Nix-TTS (Submitted to INTERSPEECH 2022). We released the pretrained models, an interactive demo, and audio samples below. [📄 Paper Link] [🤗 Interactive Demo] [📢 Audio Samples] Abstract    We propose Nix-TTS, a lightweight neural TTS (Text-to-Speech) model achieved by applying knowledge distillation to a powerful yet large-sized generative TTS teacher model. Distilling a TTS model might […]

Read more
End-to-End

End-to-end Point Cloud Correspondences with Transformers

This repository contains the source code for REGTR. REGTR utilizes multiple transformer attention layers to directly predict each downsampled point’s corresponding location in the other point cloud. Unlike typical correspondence-based registration algorithms, the predicted correspondences are clean and do not require an additional RANSAC step. This results in a fast, yet accurate registration. If you find this useful, please cite: @inproceedings{yew2022regtr, title={REGTR: End-to-end Point Cloud Correspondences with Transformers}, author={Yew, Zi Jian and Lee, Gim hee}, booktitle={CVPR}, year={2022}, } Dataset environment […]

Read more
End-to-End

An end to end deep learning method Mcformer to utilize the customer clickstream data to predict the user purchase intention

This is an end to end deep learning method Mcformer to utilize the customer clickstream data to predict the user purchase intention. We aim to utilize the customer clickstream data to predict the customer purchase intention, the scenes as follows: The framework of Mcformer Introduction of Mcformer In order to deal with multi-dimension clickstream sequence data, we proposed an end-to-end deep learning model, named Multi-channel for purchase transformer (Mcformer), to predict the customers’ purchasing intention. Figure 1 shows the model […]

Read more
End-to-End

End-to-End Object Detection with Learnable Proposal, CVPR2021

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals Paper (CVPR 2021) Sparse R-CNN: End-to-End Object Detection with Learnable Proposals Updates (02/03/2021) Higher performance is reported by using stronger backbone model PVT. (23/02/2021) Higher performance is reported by using stronger pretrain model DetCo. (02/12/2020) Models and logs(R101_100pro_3x and R101_300pro_3x) are available. (26/11/2020) Models and logs(R50_100pro_3x and R50_300pro_3x) are available. (26/11/2020) Higher performance for Sparse R-CNN is reported by setting the dropout rate as 0.0. Models Models and logs are available in […]

Read more
End-to-End

End-to-end Python framework for building natural language search interfaces to data

Haystack is an end-to-end framework that enables you to build powerful and production-ready pipelines for different search use cases. Whether you want to perform Question Answering or semantic document search, you can use the State-of-the-Art NLP models in Haystack to provide unique search experiences and allow your users to query in natural language. Haystack is built in a modular fashion so that you can combine the best technology from other open-source projects like Huggingface’s Transformers, Elasticsearch, or Milvus. What to […]

Read more
End-to-End

Deal or No Deal? End-to-End Learning for Negotiation Dialogues

end-to-end-negotiator This is a PyTorch implementation of the following research papers: The code is developed by Facebook AI Research. The code trains neural networks to hold negotiations in natural language, and allows reinforcement learning self play and rollout-based planning. If you want to use this code in your research, please cite: @inproceedings{DBLP:conf/icml/YaratsL18, author = {Denis Yarats and Mike Lewis}, title = {Hierarchical Text Generation and Planning for Strategic Dialogue}, booktitle = {Proceedings of the 35th International Conference on Machine Learning, […]

Read more