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Samples for working with Azure OpenAI Service
Code of our method MbLS (Margin-based Label Smoothing) for network calibration. To Appear at CVPR 2022. Paper : https://arxiv.org/abs/2111.15430
[ACL 2021] LM-BFF: Better Few-shot Fine-tuning of Language Models https://arxiv.org/abs/2012.15723
Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"
IPyPlot is a small python package offering fast and efficient plotting of images inside Python Notebooks. It's using IPython with HTML for faster, richer and more interactive way of displaying big …
A collection of corpora for named entity recognition (NER) and entity recognition tasks. These annotated datasets cover a variety of languages, domains and entity types.
[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs
PyTorch code for EMNLP 2020 Paper "Vokenization: Improving Language Understanding with Visual Supervision"
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
This repository contains the codes of "A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild", published at ACM Multimedia 2020. For HD commercial model, please try out Sync Labs
Content and Style Disentanglement for Artistic Style Transfer [ICCV19]
Pytorch implementation of MixNMatch
Doodle to Search: Practical Zero Shot Sketch Based Image Retrieval
Natural Language Processing Best Practices & Examples
Image augmentation for machine learning experiments.
Domain agnostic learning with disentangled representations
Hierarchical Recurrent Encoder Decoder for Query Suggestion
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
A curated list of Meta-Learning resources/papers.
A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
😎 A curated list of the Question Answering (QA)
Civic Issue Detection Dataset from Adversarial Adaptation of Scene Graph Models for Understanding Civic Issues