Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
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Updated
Feb 18, 2025 - Python
Representation learning is a set of techniques in machine learning that automatically discover compact and meaningful features from raw data. It underpins modern advances in natural language processing, computer vision, and speech recognition.
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Reading list for research topics in multimodal machine learning
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
Self-Supervised Speech Pre-training and Representation Learning Toolkit
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
A curated list of network embedding techniques.
Datasets, tools, and benchmarks for representation learning of code.
A collection of research on knowledge graphs
SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer".
The implementation of DeBERTa
Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集
Code for ALBEF: a new vision-language pre-training method
Unified Training of Universal Time Series Forecasting Transformers
LibCity: An Open Library for Urban Spatial-temporal Data Mining
EVA Series: Visual Representation Fantasies from BAAI
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations by T. Chen et al.
Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"
PyCIL: A Python Toolbox for Class-Incremental Learning