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

https://github.com/rmrisforbidden/Fooling_Neural_Network-Interpretations

Fooling Neural Network Interpretations via Adversarial Model Manipulation

https://github.com/rmwkwok/product_visual_search

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/roatienza/deep-text-recognition-benchmark

What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

https://github.com/robodhruv/visualnav-transformer

ViNT: A Foundation Model for Visual Navigation

https://github.com/roboflow/rf100-vl

Magma: A Foundation Model for Multimodal AI Agents

https://github.com/rohitgr7/tvmodels

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/rohithreddy024/Text-Summarizer-Pytorch

A Deep Reinforced Model for Abstractive Summarization

https://github.com/rorschachchen/entangled-watermark-torch

Entangled Watermarks as a Defense against Model Extraction

https://github.com/rrrlw/TDAstats

Particle gradient descent model for point process generation

https://github.com/ruberthbarros/model-comparison

Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning

https://github.com/runamu/compositional-conservatism

MOPO: Model-based Offline Policy Optimization

https://github.com/rvl-lab-utoronto/HyperCRL

Continual Model-Based Reinforcement Learning with Hypernetworks

https://github.com/rwightman/efficientnet-jax

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/rwightman/gen-efficientnet-pytorch

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/rwightman/pytorch-image-models

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/safakkbilici/Synthetic-Music-Generation-with-Deep-Neural-Networks

Jukebox: A Generative Model for Music

https://github.com/safolab-wisc/dolphins

Dolphins: Multimodal Language Model for Driving

https://github.com/safolab-wisc/fiubench

Dolphins: Multimodal Language Model for Driving

https://github.com/sajjjadayobi/ParsBigBird

ParsBERT: Transformer-based Model for Persian Language Understanding

https://github.com/salesforce/codet5

Model Cards for Model Reporting

https://github.com/samar-khanna/diffusionsat

DiffusionSat: A Generative Foundation Model for Satellite Imagery

https://github.com/samirsahoo007/Audio-and-Speech-Processing

An Unsupervised Autoregressive Model for Speech Representation Learning

https://github.com/samuelmurray/heterogeneous-gp

Mixed Likelihood Gaussian Process Latent Variable Model

https://github.com/samuelsimko/crl-llm-defense

Representation Bending for Large Language Model Safety

https://github.com/samwisegamjeee/pytorch-transformers

Cross-lingual Language Model Pretraining

https://github.com/sandeeprockstar/Pose_Estimation

An Empirical Model of Large-Batch Training

https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/sarahisyoung/rlpyt

An Empirical Model of Large-Batch Training

https://github.com/sarus-tech/tf2-published-models/tree/master/pixelsnail

PixelSNAIL: An Improved Autoregressive Generative Model

https://github.com/sauc-abadal/alt

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/sayhitosandy/Chatbot

A Neural Conversational Model

https://github.com/scpark20/universal-music-translation

WaveNet: A Generative Model for Raw Audio

https://github.com/semskurto/APTOS

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/seraphlabs-ca/SentenceMIM-demo

SentenceMIM: A Latent Variable Language Model

https://github.com/serbanstan/mas3-continual

Unsupervised Model Adaptation for Continual Semantic Segmentation

https://github.com/setharram/facenet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

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