Paper Citation Record · LEDGER

Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes

As of 14 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2412.02596.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.02596 v1

Coverage vector

measured 50 of 50 reference resolution

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measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation 9a94ef8c-f167-4c28-a1fa-e8fcf4bd931e · outbound

This paper cites @esa (Ref.

Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes @esa (Ref

Reference 1

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Food-101 -- mining discriminative components with random forests

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Bourlard and Y

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Remote sensing image scene classification: Benchmark and state of the art

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Cimpoi, S

Reference 7

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Imagenet: A large-scale hierarchical image database

Reference 9

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Understanding dataset difficulty with v-usable information

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This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories.

Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Caltech-256 object category dataset

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This paper cites Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 13

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Autoencoders, minimum description length and helmholtz free energy

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Papadopoulos, and Vittorio Ferrari

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Scaling Laws for Neural Language Models

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Novel dataset for fine-grained image categorization

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Auto-Encoding Variational Bayes

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Learning multiple layers of features from tiny images

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Mnist handwritten digit database

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Oxford University

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Umap: Uniform manifold approximation and projection

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Automated flower classification over a large number of classes

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Confident learning: Estimating uncertainty in dataset labels

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Konovalov, Bronson Philippa, Peter Ridd, Jake C

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Elenberg, and Kilian Q

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Estimating training data influence by tracing gradient descent

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Quattoni and A

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Improving language understanding by generative pre-training

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Learning Transferable Visual Models From Natural Language Supervision

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Real-Time Flying Object Detection with YOLOv8

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes High-Resolution Image Synthesis with Latent Diffusion Models

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Rumelhart, Geoffrey E

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Parametric umap embeddings for representation and semisupervised learning

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes An Empirical Study of Automated Mislabel Detection in Real World Vision Datasets

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Extracting and composing robust features with denoising autoencoders

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Xiao , J

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Medmnist v2 - a large-scale lightweight benchmark for 2d and 3d biomedical image classification

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes DIME : An information-theoretic difficulty measure for AI datasets

Reference 48

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Places: A 10 million image database for scene recognition

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Detecting corrupted labels without training a model to predict

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models

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Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes write newline

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Pith citing papers

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