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Memorization

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Wide_and_Deep_Multitable

Wide & Deep Learning for Recommender Systems

https://github.com/2023-MindSpore-4/Code8/tree/main/cct

Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations

https://github.com/5m0k3/gwd-efficientdet-pytorch

mixup: Beyond Empirical Risk Minimization

https://github.com/5m0k3/gwd-yolov5-pytorch

mixup: Beyond Empirical Risk Minimization

https://github.com/AngusG/bn-advex-zhang-fixup

mixup: Beyond Empirical Risk Minimization

https://github.com/AutoML-4Paradigm/S2E

Searching to Exploit Memorization Effect in Learning from Corrupted Labels

https://github.com/Bennie-Han/Image-augementation-pytorch

mixup: Beyond Empirical Risk Minimization

https://github.com/Brunogomes97/Imdb

mixup: Beyond Empirical Risk Minimization

https://github.com/CaoShuning/MIXUP

mixup: Beyond Empirical Risk Minimization

https://github.com/DATEXIS/AMEGA-benchmark

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

https://github.com/DiegoOrtego/LabelNoiseDRPL

Towards Robust Learning with Different Label Noise Distributions

https://github.com/DiegoOrtego/LabelNoiseMOIT

Multi-Objective Interpolation Training for Robustness to Label Noise

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

mixup: Beyond Empirical Risk Minimization

https://github.com/GitHub-HongweiZhang/prediction-flow

Wide & Deep Learning for Recommender Systems

https://github.com/Hazelsuko07/InstaHide

mixup: Beyond Empirical Risk Minimization

https://github.com/JeremyFisher/few_shot_3dr

Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors

https://github.com/JunweiLiang/Object_Detection_Tracking

mixup: Beyond Empirical Risk Minimization

https://github.com/Kabir5296/CSTA-CustomImplementation

CSTA: Spatial-Temporal Causal Adaptive Learning for Exemplar-Free Video Class-Incremental Learning

https://github.com/KarhouTam/FL-bench

Personalized Federated Learning through Local Memorization

https://github.com/LeeHyeJin91/Wide_and_Deep

Wide & Deep Learning for Recommender Systems

https://github.com/LeiLiu-byte/the-Essence-of-LT-Rocognition

mixup: Beyond Empirical Risk Minimization

https://github.com/Lightning-AI/lit-gpt

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

https://github.com/MS-Mind/MS-Code-06/tree/main/Neighbor2Neighbor

Generalization through Memorization: Nearest Neighbor Language Models

https://github.com/MindCode-4/code-12/tree/main/Learning-with-Noisy-Labels

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

https://github.com/MindSpore-paper-code-3/code6/tree/main/cct

Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations

https://github.com/MindSpore-scientific-2/code-3/tree/main/wide-minima-density-hypothesis

Wide & Deep Learning for Recommender Systems

https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/WideAndDeep

Wide & Deep Learning for Recommender Systems

https://github.com/NeurAI-Lab/UniNet

Noisy Concurrent Training for Efficient Learning under Label Noise

https://github.com/OFRIN/Tensorflow_MixUp

mixup: Beyond Empirical Risk Minimization

https://github.com/OfirKedem/Pointer-Value-Retrieval

Pointer Value Retrieval: A new benchmark for understanding the limits of neural network generalization

https://github.com/P2333/Mixup-Inference

mixup: Beyond Empirical Risk Minimization

https://github.com/PaddlePaddle/PaddleClas

mixup: Beyond Empirical Risk Minimization

https://github.com/PaddlePaddle/PaddleDetection

mixup: Beyond Empirical Risk Minimization

https://github.com/PaddlePaddle/PaddleRec/tree/release/2.1.0/models/rank/wide_deep

Wide & Deep Learning for Recommender Systems

https://github.com/PsorTheDoctor/microarray-data

mixup: Beyond Empirical Risk Minimization

https://github.com/RUCAIBox/R1-Searcher

R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.