Python Meta-pretraining Resources

LinkBERT: A Knowledgeable Language Model Pretrained with Document Links This repo provides the model, code & data of our paper: LinkBERT: Pretraining

BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training By Likun Cai, Zhi Zhang, Yi Zhu, Li Zhang, Mu Li, Xiangyang Xue. This

Vision-Language Pre-Training with Triple Contrastive Learning, CVPR 2022 News (03/16/2022) upload retrieval checkpoints finetuned on COCO and Flickr T

data2vec-pytorch PyTorch implementation of "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI (F

Paper | Blog OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image gene

AMOS This repository contains the scripts for fine-tuning AMOS pretrained models on GLUE and SQuAD 2.0 benchmarks. Paper: Pretraining Text Encoders wi

Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data (CVPR 2022) Potentials of primitive shapes f

Continuous-Time Meta-Learning with Forward Mode Differentiation ICLR 2022 (Spotlight) - Installation - Example - Citation This repository contains the

Auto-Lambda This repository contains the source code of Auto-Lambda and baselines from the paper, Auto-Lambda: Disentangling Dynamic Task Relationship

Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

Task-Informed Meta-Learning This repository contains examples of Task-Informed Meta-Learning (paper). We consider two tasks: Crop Type Classification

Evolve plastic networks to be able to automatically acquire novel cognitive (meta-learning) tasks

RuCLIPtiny Zero-shot image classification model for Russian language RuCLIP tiny (Russian Contrastive Language–Image Pretraining) is a neural network

ruCLIP-SB RuCLIP-SB (Russian Contrastive Language–Image Pretraining SWIN-BERT) is a multimodal model for obtaining images and text similarities and re

MetaTTE: a Meta-Learning Based Travel Time Estimation Model for Multi-city Scenarios This is the official TensorFlow implementation of MetaTTE in the

OntoProtein This is the implement of the paper "OntoProtein: Protein Pretraining With Ontology Embedding". OntoProtein is an effective method that mak

Noether Networks: meta-learning useful conserved quantities This repository contains the code necessary to reproduce experiments from "Noether Network

PyTorch META-DATASET (Few-shot classification benchmark) This repo contains a PyTorch implementation of meta-dataset and a unified implementation of s

Crypto Meta Extractor This repository contains the code which extracts some metadata of all the cryptocurrencies listed (9K) on CoinMarketCap. Coding

FSL-Mate is a collection of resources for few-shot learning (FSL). In particular, FSL-Mate currently contains FewShotPapers: a paper list which tracks

ECLARE ECLARE: Extreme Classification with Label Graph Correlations @InProceedings{Mittal21b, author = "Mittal, A. and Sachdeva, N. and Agrawal

Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark Project | Arxiv | YouTube | | Abstract In recent years, deep learning-based methods

Meta learning algorithms to train cross-lingual NLI (multi-task) models

Source codes for Improved Few-Shot Visual Classification (CVPR 2020), Enhancing Few-Shot Image Classification with Unlabelled Examples (WACV 2022) and Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning (TPAMI 2022 - in submission)

MetaCall Polyglot REPL Description This repository implements a Polyglot REPL which shares the state of the meta-object protocol between the REPLs. Us

1 Meta-FDMIxup Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data. (ACM MM 2021) paper News! the rep

Fast Training of Neural Lumigraph Representations using Meta Learning Project Page | Paper | Data Alexander W. Bergman, Petr Kellnhofer, Gordon Wetzst

Meta Learning Backpropagation And Improving It (VSML) This is research code for the NeurIPS 2021 publication Kirsch & Schmidhuber 2021. Many concepts

abcmeta Python meta class and abstract method library with restrictions. This library provides a restricted way to validate abstract methods. The Pyth

MT-GAN-PyTorch PyTorch Implementation of AAAI-2020 Paper "Learning to Transfer: Unsupervised Domain Translation via Meta-Learning" Dependency: Python

Web-Scraping-COVID-19-Meta-Portal-with-Python - Requests API and Beautiful Soup to scrape real-time COVID statistics from worldometer website and perform data cleaning and visual analysis in Jupyter notebook.

few-shot-ssl-public Code for paper Meta-Learning for Semi-Supervised Few-Shot Classification. [arxiv] Dependencies cv2 numpy pandas python 2.7 / 3.5+

Pretraining on Dynamic Graph Neural Networks Our article is PT-DGNN and the code is modified based on GPT-GNN Requirements python 3.6 Ubuntu 18.04.5 L

Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs This repository is the implementation of SELAR. Dasol Hwang* , Jinyoung Pa

Synthetic dataset rendering Framework for producing the synthetic datasets used in: How Useful is Self-Supervised Pretraining for Visual Tasks? Alejan

Base Script Python is an excellent language that makes writing scripts very straightforward. Over the course of writing many scripts, we realized that

MetaMove MetaMove is written in Python3 and aims at easing batch renaming operations based on file meta data. MetaMove abuses eval combined with f-str

Offline Meta-Reinforcement Learning with Advantage Weighting (MACAW) MACAW code used for the experiments in the ICML 2021 paper. Installing the enviro

magiCARP: Contrastive Authoring+Reviewing Pretraining Welcome to the magiCARP API, the test bed used by EleutherAI for performing text/text bi-encoder

Splinter This repository contains the code, models and datasets discussed in our paper "Few-Shot Question Answering by Pretraining Span Selection", to

A State-of-the-Art Large-scale Pretrained Response Generation Model (DialoGPT) This repository contains the source code and trained model for a large-

[Re] Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Reimplementation of NeurIPS'19: "Meta-Weight-Net: Learning an Explicit Mapping

MMO: Meta Multi-Objectivization for Software Configuration Tuning This repository contains the data and code for the following paper that is currently

FinRL-Meta: A Universe of Market Environments. FinRL-Meta is a universe of market environments for data-driven financial reinforcement learning. Users

Easy Language Model Pretraining leveraging Huggingface's Transformers and Datasets What is LASSL • How to Use What is LASSL LASSL은 LAnguage Semi-Super

DiffMG This repository contains the code for our KDD 2021 Research Track paper: DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neura

Meta-Weight-Net NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Official Pytorch implementation for noisy labels). The

Balanced Meta-Softmax Code for the paper Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu

MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition (CVPR 2021) arXiv Prerequisite PyTorch = 1.2.0 Python3 torchvision PIL argpar

COCO-LM This repository contains the scripts for fine-tuning COCO-LM pretrained models on GLUE and SQuAD 2.0 benchmarks. Paper: COCO-LM: Correcting an

Curriculum Meta-Learning for Few-shot Classification We propose an adaptation of the curriculum training framework, applicable to state-of-the-art met

VILLA: Vision-and-Language Adversarial Training This is the official repository of VILLA (NeurIPS 2020 Spotlight). This repository currently supports

CLIP [Blog] [Paper] [Model Card] [Colab] CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pair

Autoencoders pretraining using clustering

pretraining-learning-curves This is the repository for the paper When Do You Need Billions of Words of Pretraining Data? Edge Probing We use jiant1 fo

dont-stop-pretraining Code associated with the Don't Stop Pretraining ACL 2020 paper Citation @inproceedings{dontstoppretraining2020, author = {Suchi

Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning This is the official repository of "Camera Distortion-

[NeurIPS 2021 Spotlight] HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning [Paper] This is Official PyTorch implementatio

Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021) In this repository we provide PyTorch implementations for GeMCL; a

CryptoForecasting using Machine and Deep learning (Part 1) CryptoForecasting using Machine Learning The main aspect of predicting the stock-related da

Meta-Learning to Improve Pre-Training This folder contains code to run experiments in the paper Meta-Learning to Improve Pre-Training, NeurIPS 2021. P

FAME: Feature-based Adversarial Meta-Embeddings This is the companion code for the experiments reported in the paper "FAME: Feature-Based Adversarial

SSR (NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductivefew-shot classification" [Paper] [Project webpage]

L2F - Learning to Forget for Meta-Learning Sungyong Baik, Seokil Hong, Kyoung Mu Lee Source code for CVPR 2020 paper "Learning to Forget for Meta-Lear

Jittor: a Just-in-time(JIT) deep learning framework Quickstart | Install | Tutorial | Chinese Jittor is a high-performance deep learning framework bas

MeTAL - Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning (ICCV2021 Oral) Sungyong Baik, Janghoon Choi, Heewon Kim, Dohee Cho, Jaes

fast.ai ULMFiT with SentencePiece from pretraining to deployment Motivation: Why even bother with a non-BERT / Transformer language model? Short answe

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. Hyperactive: is very easy to lear

A repository to compare many popular NAS algorithms seamlessly across three popular benchmarks (NASBench 101, 201, and 301). You can implement your ow

Introduction XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective.

Robust Self-augmentation for NER with Meta-reweighting

PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int

Code for paper "Context-self contrastive pretraining for crop type semantic segmentation" Setting up a python environment Follow the instruction in ht

PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation Introduction Getting Started FSD50K Recipe AudioSet Recipe Label E

TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code

TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning Authors: Yixuan Su, Fangyu Liu, Zaiqiao Meng, Lei Shu, Ehsan Shareghi, and Nig

StackNet This repository contains StackNet Meta modelling methodology (and software) which is part of my work as a PhD Student in the computer science

CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to

napari-time-slicer A meta plugin for processing timelapse data timepoint by timepoint. It enables a list of napari plugins to process 2D+t or 3D+t dat

M.E.A.T. - Mobile Evidence Acquisition Toolkit Meet M.E.A.T! From Jack Farley - BlackStone Discovery This toolkit aims to help forensicators perform d

Meta-TTS: Meta-Learning for Few-shot SpeakerAdaptive Text-to-Speech This repository is the official implementation of "Meta-TTS: Meta-Learning for Few

TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning Authors: Yixuan Su, Fangyu Liu, Zaiqiao Meng, Lei Shu, Ehsan Shareghi, and Nig

Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems This is our experimental code for RecSys 2021 paper "Learning

NLP From Scratch Without Large-Scale Pretraining This repository contains the code, pre-trained model checkpoints and curated datasets for our paper:

Generalized Data Weighting via Class-level Gradient Manipulation This repository is the official implementation of Generalized Data Weighting via Clas

ATS About Source code of the paper Meta-learning with an Adaptive Task Scheduler. If you find this repository useful in your research, please cite the

Generalized Data Weighting via Class-level Gradient Manipulation This repository is the official implementation of Generalized Data Weighting via Clas

META-RS This is the companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsu

Deep-FIR Codebase - Super Resolution Meta Attention Networks About This repository contains the main coding framework accompanying our work on meta-at

Meta-SparseINR Official PyTorch implementation of "Meta-learning Sparse Implicit Neural Representations" (NeurIPS 2021) by Jaeho Lee*, Jihoon Tack*, N

Learning where to learn - Gradient sparsity in meta and continual learning In this paper, we investigate gradient sparsity found by MAML in various co

Multi-Task Meta-Learning Modification with Stochastic Approximation This repository contains the code for the paper "Multi-Task Meta-Learning Modifica

Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis Requirements python 3.7 pytorch-gpu 1.7 numpy 1.19.4 pytorch_

NeurIPS 2021 (Spotlight): Task-Adaptive Neural Network Search with Meta-Contrastive Learning This is an official PyTorch implementation of Task-Adapti

🌟 Sparse Spatial Transformers for Few-Shot Learning This code implements the Sparse Spatial Transformers for Few-Shot Learning(SSFormers). Our code i

Louise - polynomial-time Program Learning Getting help with Louise Louise's author can be reached by email at [email protected]. Please use this email t

Incremental Object Detection via Meta-Learning To appear in an upcoming issue of the IEEE Transactions on Pattern Analysis and Machine Intelligence (T

ST This is the code of NeurIPS 2021 paper "Towards Enabling Meta-Learning from Target Models". If you use any content of this repo for your work, plea

SoCo [NeurIPS 2021 Spotlight] Aligning Pretraining for Detection via Object-Level Contrastive Learning By Fangyun Wei*, Yue Gao*, Zhirong Wu, Han Hu,