Papers with Code 아카이브

Memorization

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

1

보존된 평가 기록

3

코드

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

https://github.com/julilien/memorizationdilation

공식 코드

Memorization-Dilation: Modeling Neural Collapse Under Label Noise

https://github.com/julilien/mitigatinglabelnoisedataambiguation

공식 코드

Mitigating Label Noise through Data Ambiguation

https://github.com/jybai/generative-memorization-benchmark

공식 코드

On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition

https://github.com/jybai/mrgan

공식 코드

Reducing Training Sample Memorization in GANs by Training with Memorization Rejection

https://github.com/kairanzhao/rum

공식 코드

Scalability of memorization-based machine unlearning

https://github.com/kaistai/factual-knowledge-acquisition

공식 코드

How Do Large Language Models Acquire Factual Knowledge During Pretraining?

https://github.com/kaistai/knowledge-reasoning

공식 코드

Hierarchical Deconstruction of LLM Reasoning: A Graph-Based Framework for Analyzing Knowledge Utilization

https://github.com/kangningthu/adele

공식 코드

Adaptive Early-Learning Correction for Segmentation from Noisy Annotations

https://github.com/katiekang1998/reasoning_generalization

공식 코드

What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?

https://github.com/kawatami/dner

공식 코드

Dynamic Named Entity Recognition

https://github.com/keikeiqi/mgtta

공식 코드

Learning to Generate Gradients for Test-Time Adaptation via Test-Time Training Layers

https://github.com/keremzaman/fusetoforget

공식 코드

Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion

https://github.com/kilian-group/lmlm

공식 코드

Pre-training Large Memory Language Models with Internal and External Knowledge

https://github.com/kli8996/HSISR

공식 코드

Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets

https://github.com/kobeshegu/awesome-few-shot-generation

공식 코드

Image Synthesis under Limited Data: A Survey and Taxonomy

https://github.com/kobeshegu/fregan_neurips2022

공식 코드

FreGAN: Exploiting Frequency Components for Training GANs under Limited Data

https://github.com/labforcomputationalvision/memorization_generalization_in_diffusion_models

공식 코드

Generalization in diffusion models arises from geometry-adaptive harmonic representations

https://github.com/layer6ai-labs/dgm-eval

공식 코드

Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

https://github.com/leodana2000/Transformer_Attentional_Memory

공식 코드

Memorization in Attention-only Transformers

https://github.com/lgalke/easy2deeplearn

공식 코드

What makes a language easy to deep-learn? Deep neural networks and humans similarly benefit from compositional structure

https://github.com/liangzid/promptextractioneval

공식 코드

Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models

https://github.com/lijichang/flnl-feddiv

공식 코드

FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels

https://github.com/linghan1997/regression-based-analytic-incremental-learning

공식 코드

Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models

https://github.com/linhaowei1/kumo

공식 코드

Generative Evaluation of Complex Reasoning in Large Language Models

https://github.com/liupei101/psemix

공식 코드

Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification

https://github.com/liuxiao-guan/iet_agc

공식 코드

Iterative Ensemble Training with Anti-Gradient Control for Mitigating Memorization in Diffusion Models

https://github.com/liuzhuang13/bias

공식 코드

A Decade's Battle on Dataset Bias: Are We There Yet?

https://github.com/lixiaothu/privacy_and_aug

공식 코드

On the Privacy Effect of Data Enhancement via the Lens of Memorization

https://github.com/liyi01827/noisy-contrastive

공식 코드

On Learning Contrastive Representations for Learning with Noisy Labels

https://github.com/lizardp1/diffusion_low_noise_regime

공식 코드

Diffusion models under low-noise regime

https://github.com/ljm565/tesgan

공식 코드

Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesis

https://github.com/lujunru/memochat

공식 코드

MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation

https://github.com/luka-group/causal-view-of-entity-bias

공식 코드

A Causal View of Entity Bias in (Large) Language Models

https://github.com/lukaborec/memorization-nucleus-sampling

공식 코드

The Unreasonable Ineffectiveness of Nucleus Sampling on Mitigating Text Memorization

https://github.com/m-resta/sg-rep

공식 코드

Self-generated Replay Memories for Continual Neural Machine Translation

https://github.com/mac-automl/abstract-reason-benchmark

공식 코드

Benchmarking Abstract and Reasoning Abilities Through A Theoretical Perspective

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