Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping.
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Updated
Nov 3, 2023
Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping.
Custom groovy scripts for QuaPath
Pre-training a VisionTransformer with Masked Image Modelling for semantic segmentation
Self-Supervised Representation Learning of Semiconductor Wafer Maps using PyTorch
code for "AdPE: Adversarial Positional Embeddings for Pretraining Vision Transformers via MAE+"
Pytorch reimplementation of "A Unified View of Masked Image Modeling".
Pytorch implementation of an energy transformer - an energy-based reccurrent variant of the transformer.
Official codebase for "Unveiling the Power of Audio-Visual Early Fusion Transformers with Dense Interactions through Masked Modeling".
Official implementation of Matrix Variational Masked Autoencoder (M-MAE) for ICML paper "Information Flow in Self-Supervised Learning" (https://arxiv.org/abs/2309.17281)
PyTorch implementation for "Training and Inference on Any-Order Autoregressive Models the Right Way", NeurIPS 2022 Oral, TPM 2023 Best Paper Honorable Mention
Code to reproduce experiments from the paper "Continual Pre-Training Mitigates Forgetting in Language and Vision" https://arxiv.org/abs/2205.09357
Code of CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping
[NeurIPS 2023] Masked Image Residual Learning for Scaling Deeper Vision Transformers
[ICML 2023] Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN
[ECCV 2022] Official pytorch implementation of "mc-BEiT: Multi-choice Discretization for Image BERT Pre-training" in European Conference on Computer Vision (ECCV) 2022.
Recent Advances in Vision-Language Pre-training!
Official Code of the paper "Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing"
PyTorch reimplementation of "A simple, efficient and scalable contrastive masked autoencoder for learning visual representations".
[ICLR2024] Exploring Target Representations for Masked Autoencoders
This is a PyTorch implementation of “Context AutoEncoder for Self-Supervised Representation Learning"
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