Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets | Paper Citation Record

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This paper cites In: International Conference on Machine Learning.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: International Conference on Machine Learning

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This paper cites Dataset Distillation by Matching Training Trajectories.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Dataset Distillation by Matching Training Trajectories

Reference 2

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This paper cites Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality

Reference 3

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This paper cites Dataset Distillers Are Good Label Denoisers In the Wild.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Dataset Distillers Are Good Label Denoisers In the Wild

Reference 4

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This paper cites DC-BENCH: Dataset Condensation Benchmark.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets DC-BENCH: Dataset Condensation Benchmark

Reference 5

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This paper cites Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation

Reference 6

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This paper cites An Embedding is Worth a Thousand Noisy Labels.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets An Embedding is Worth a Thousand Noisy Labels

Reference 7

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This paper cites Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation

Reference 8

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This paper cites In: ICLR 2024-The Twelfth International Conference on Learning Representations, Messe Wien Exhibition and Congress Center, Vienna, Austria, May 7-11t, 2024 (2024).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: ICLR 2024-The Twelfth International Conference on Learning Representations, Messe Wien Exhibition and Congress Center, Vienna, Austria, May 7-11t, 2024 (2024)

Reference 9

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This paper cites IEEE Transactions on Neural Networks and Learning Systems35(11), 16036–16048 (2023).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets IEEE Transactions on Neural Networks and Learning Systems35(11), 16036–16048 (2023)

Reference 10

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This paper cites IEEE Transactions on Medical Imaging42(6), 1720– 1734 (2023).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets IEEE Transactions on Medical Imaging42(6), 1720– 1734 (2023)

Reference 11

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This paper cites Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching

Reference 12

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This paper cites Advances in neural information processing systems31(2018).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Advances in neural information processing systems31(2018)

Reference 13

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This paper cites Multisize Dataset Condensation.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Multisize Dataset Condensation

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This paper cites 2022 ieee.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets 2022 ieee

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This paper cites In: International conference on machine learning.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: International conference on machine learning

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Unresolved cited work

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This paper cites CS 231N7(7), 3 (2015).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets CS 231N7(7), 3 (2015)

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This paper cites SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching

Reference 19

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This paper cites In: European Conference on Computer Vision.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: European Conference on Computer Vision

Reference 20

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This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets DivideMix: Learning with Noisy Labels as Semi-supervised Learning

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This paper cites WebVision Database: Visual Learning and Understanding from Web Data.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets WebVision Database: Visual Learning and Understanding from Web Data

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This paper cites Advances in neural information processing systems33, 20331–20342 (2020).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Advances in neural information processing systems33, 20331–20342 (2020)

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This paper cites In: International conference on machine learning.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: International conference on machine learning

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This paper cites Dataset Distillation with Convexified Implicit Gradients.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Dataset Distillation with Convexified Implicit Gradients

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This paper cites Curriculum Loss: Robust Learning and Generalization against Label Corruption.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Curriculum Loss: Robust Learning and Generalization against Label Corruption

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This paper cites Advances in Neural Information Processing Systems 35, 30044–30057 (2022).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Advances in Neural Information Processing Systems 35, 30044–30057 (2022)

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets when to update

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Dataset Distillation with Infinitely Wide Convolutional Networks

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This paper cites Training Deep Neural Networks on Noisy Labels with Bootstrapping.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Training Deep Neural Networks on Noisy Labels with Bootstrapping

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Data Distillation: A Survey

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Advances in neural information processing systems32(2019) 18 Kaifeng Chen et al

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This paper cites In: International conference on machine learning.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: International conference on machine learning

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This paper cites IEEE transactions on neural networks and learning systems34(11), 8135–8153 (2022).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets IEEE transactions on neural networks and learning systems34(11), 8135–8153 (2022)

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Dataset Distillation with Neural Characteristic Function: A Minmax Perspective

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This paper cites In: 2018 IEEE 30th international conference on tools with artificial intelligence (ICTAI).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: 2018 IEEE 30th international conference on tools with artificial intelligence (ICTAI)

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Dataset Distillation

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This paper cites In: European Conference on Computer Vision.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: European Conference on Computer Vision

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This paper cites Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets DANCE: Dual-View Distribution Alignment for Dataset Condensation

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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This paper cites Advances in neural information process- ing systems31(2018).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Advances in neural information process- ing systems31(2018)

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Dataset Condensation with Distribution Matching

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Dataset Condensation with Gradient Matching

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This paper cites In: International Conference on Learning Representations (2021).

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: International Conference on Learning Representations (2021)

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Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Dataset Distillation using Neural Feature Regression

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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This paper cites In: International conference on machine learning.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets In: International conference on machine learning

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