Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
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Updated
May 22, 2024 - Python
Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
[RA-L, 2024] The dataset for the paper: Learning self-supervised traversability with navigation experiences of mobile robots: A risk-aware self-training approach
🍿POPCORN: High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2 🌍🛰️
Repository for "Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification""
Official Pytorch Code of Our Paper: Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need
Analysis of 3D pathology samples using weakly supervised AI - Cell
Official implementation of "Multi-scale Bottleneck Transformer for Weakly Supervised Multimodal Violence Detection"
a collection of small machine learning projects
Simulation code for "Ordinal Multiple Instance Support Vector Machines"
[ICLR 2024] SemiReward: A General Reward Model for Semi-supervised Learning
[CVPR 2022] C2AM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
[CVPR 2022] CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation
Recent weakly supervised semantic segmentation paper
Inference of Disease Progressive Level in Single-Cell Data
Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image
Code for the paper " PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers "
Supplementary material and code for "Mitigating Label Noise through Data Ambiguation" as published at AAAI 2024.
This repo for the paper titled "SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification"
mapping land cover at a national scale, using Sentinel-2 imagery and weak labels from CORINE land cover data
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