PyTorch implementation of MADE
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Updated
Oct 24, 2020 - Python
PyTorch implementation of MADE
ALBERT trained on Mongolian text corpus
reproduction of semantic segmentation using masked autoencoder (mae)
PyTorch wrapper for Deep Density Estimation Models
PyTorch implementation of BEVT (CVPR 2022) https://arxiv.org/abs/2112.01529
Unofficial PyTorch implementation of Masked Autoencoders that Listen
Understanding Self-Supervised Learning in a Decentralized Setting
Official Codes for "Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality"
Official Pytorch implementation of Efficient Video Representation Learning via Masked Video Modeling with Motion-centric Token Selection.
Train MAE on Kaggle 2 GPUs (T4x2), Log to Wandb
[NeurIPS 2022 Spotlight] VideoMAE for Action Detection
Extraction of deep features/representation of birds by deep learning algorithms.
code for "AdPE: Adversarial Positional Embeddings for Pretraining Vision Transformers via MAE+"
Official implementation of "A simple, efficient and scalable contrastive masked autoencoder for learning visual representations".
Change detection on satellite images with masked autoencoders.
Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representations
Codebase for Imperial MSc AI Individual Project - Self-Supervised Learning for Audio Inference
[CVPR2023] Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning (https://arxiv.org/abs/2212.04500)
[SIGIR'2023] "MAERec: Graph Masked Autoencoder for Sequential Recommendation"
The code for the paper "Contrastive Masked Autoencoders for Self-Supervised Video Hashing" (AAAI'23)
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