https://github.com/samchevalier/dcattack
공식 코드Identifying the Smallest Adversarial Load Perturbations that Render DC-OPF Infeasible
Papers with Code 아카이브
Data-free Knowledge Distillation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sameerkhanna786/cvueba
공식 코드https://github.com/samia067/crisp
공식 코드CriSp: Leveraging Tread Depth Maps for Enhanced Crime-Scene Shoeprint Matching
https://github.com/samia067/shoerinsics
공식 코드Creating a Forensic Database of Shoeprints from Online Shoe Tread Photos
https://github.com/samjia2000/optimal-reasoning-efficiency
공식 코드https://github.com/samuelepapa/fit-a-nef
공식 코드How to Train Neural Field Representations: A Comprehensive Study and Benchmark
https://github.com/sanjaykariyappa/maze
공식 코드MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation
https://github.com/sariyanidi/M3DFB
공식 코드3D Face Reconstruction Error Decomposed: A Modular Benchmark for Fair and Fast Method Evaluation
https://github.com/sarthaxxxxx/av-c-robustness-benchmark
공식 코드https://github.com/sarwanpasha/adversarial_attack_on_biological_sequences
공식 코드Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification
https://github.com/satgoy152/ling_llm
공식 코드https://github.com/sbelharbi/sr-caco-2
공식 코드SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution
https://github.com/sbi-benchmark/sbibm
공식 코드https://github.com/schaepermeier/moleopt
공식 코드MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes
https://github.com/schaepermeier/r2-revisited
공식 코드Reinvestigating the R2 Indicator: Achieving Pareto Compliance by Integration
https://github.com/schmitt-muc/SEN12MS
공식 코드Remote Sensing Image Classification with the SEN12MS Dataset
https://github.com/schneiderkamplab/syntheval
공식 코드SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data
https://github.com/schuture/benchmarking-awesome-diffusion-models
공식 코드https://github.com/schuture/deeptumorvqa
공식 코드https://github.com/sci-assess/sciassess
공식 코드SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis
https://github.com/scikit-adaptation/skada-bench
공식 코드https://github.com/scmaree/HillVallEA
공식 코드https://github.com/scottclowe/zs-ssl-clustering
공식 코드An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders
https://github.com/scut-dlvclab/megahan97k
공식 코드https://github.com/sdnr/RNN-Conv-Decoder
공식 코드On Recurrent Neural Networks for Sequence-based Processing in Communications
https://github.com/se-hwan/pbrs-humanoid
공식 코드Benchmarking Potential Based Rewards for Learning Humanoid Locomotion
https://github.com/sea-shunned/hawks
공식 코드HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis
https://github.com/sea-shunned/hawks_configs
공식 코드HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis
https://github.com/searchivarius/long_doc_rank_model_analysis
공식 코드https://github.com/sebbarb/time_aware_attention
공식 코드https://github.com/sec-bench/sec-bench
공식 코드SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks
https://github.com/seedatnabeel/h-cat
공식 코드https://github.com/seekingdream/ppm
공식 코드PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models
https://github.com/seekingdream/static-to-dynamic-llmeval
공식 코드https://github.com/seephys/seephys-project
공식 코드SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning
https://github.com/selenashe/scone
공식 코드ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.