Papers with Code 아카이브

Model Optimization

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To Optimize already existing models in Training/Inferencing tasks.

코드

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

https://github.com/vanint/core-tuning

공식 코드

Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning

https://github.com/vbayeslab/deepvol

공식 코드

Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting

https://github.com/vdigpku/cmua-watermark

공식 코드

CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes

https://github.com/vdigpku/t-sea

공식 코드

T-SEA: Transfer-based Self-Ensemble Attack on Object Detection

https://github.com/vishnutez/in-context-estimation

공식 코드

Transformers are Provably Optimal In-context Estimators for Wireless Communications

https://github.com/wangxb96/eode

공식 코드

Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner

https://github.com/watermeloncq/drl-dynamic-optimization-portfolio-SB3py37

공식 코드

A Deep Reinforcement Learning Framework for Dynamic Portfolio Optimization: Evidence from China's Stock Market

https://github.com/wollf2008/IKD-MIL

공식 코드

Enhancing Weakly-Supervised Histopathology Image Segmentation with Knowledge Distillation on MIL-Based Pseudo-Labels

https://github.com/wollf2008/fw-rd

공식 코드

Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data

https://github.com/word-ky/Edge-TOOLBOX

공식 코드

Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment

https://github.com/wufangr/must

공식 코드

MUST: Multi-Scale Structural-Temporal Link Prediction Model for UAV Ad Hoc Networks

https://github.com/wxwxwwxxx/manineg

공식 코드

ManiNeg: Manifestation-guided Multimodal Pretraining for Mammography Classification

https://github.com/wzfhaha/grand-plus

공식 코드

GRAND+: Scalable Graph Random Neural Networks

https://github.com/xj-zhu98/krdn

공식 코드

Knowledge-refined Denoising Network for Robust Recommendation

https://github.com/xmed-lab/fed-mas

공식 코드

Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification

https://github.com/xtotodilex/deep-learning-model-optimization

공식 코드

Benchmarking Deep Learning Models on NVIDIA Jetson Nano for Real-Time Systems: An Empirical Investigation

https://github.com/yanglei18/mix-teaching

공식 코드

Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection

https://github.com/yao-papercodes/aglrls

공식 코드

Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition

https://github.com/yaohungt/GSTEG_CVPR_2019

공식 코드

Video Relationship Reasoning using Gated Spatio-Temporal Energy Graph

https://github.com/yifeilin0226/fastlogad

공식 코드

FastLogAD: Log Anomaly Detection with Mask-Guided Pseudo Anomaly Generation and Discrimination

https://github.com/yingtaoluo/highly-nonlinear-pde-discovery

공식 코드

Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations

https://github.com/yukang2017/Stitcher

공식 코드

Dynamic Scale Training for Object Detection

https://github.com/zbf1991/A2GNN

공식 코드

Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation

https://github.com/zhangbin-ai/apl

공식 코드

Object-aware Adaptive-Positivity Learning for Audio-Visual Question Answering

https://github.com/zhanlo/dts

공식 코드

Diverse Teacher-Students for Deep Safe Semi-Supervised Learning under Class Mismatch

https://github.com/zhichunguo/Meta-MGNN

공식 코드

Few-Shot Graph Learning for Molecular Property Prediction

https://github.com/zhiyuan-li-john/rapsg

공식 코드

Exploring Annotation-free Image Captioning with Retrieval-augmented Pseudo Sentence Generation

https://gitlab.com/eidheim/online-arbitrary-shaped-clustering

공식 코드

Online Arbitrary Shaped Clustering through Correlated Gaussian Functions

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