https://github.com/adnanmunir294/uavd-cbra
공식 코드Investigation of UAV Detection in Images with Complex Backgrounds and Rainy Artifacts
Papers with Code 아카이브
Data-free Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 2
- 보존된 평가 기록
- 8
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/adpartin/cross-dataset-drp-paper
공식 코드https://github.com/advanced-vision-and-learning-lab/sariad
공식 코드Benchmarking Suite for Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) Algorithms
https://github.com/adverml/advopenood
공식 코드Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors
https://github.com/aemiliusretiarius/labcat
공식 코드LABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regions
https://github.com/aflah02/quench
공식 코드QUENCH: Measuring the gap between Indic and Non-Indic Contextual General Reasoning in LLMs
https://github.com/afrah/QCPINN
공식 코드QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs
https://github.com/afzalxo/accel-nasbench
공식 코드Accel-NASBench: Sustainable Benchmarking for Accelerator-Aware NAS
https://github.com/agi-inc/agisdk
공식 코드REAL: Benchmarking Autonomous Agents on Deterministic Simulations of Real Websites
https://github.com/agi-inc/real
공식 코드REAL: Benchmarking Autonomous Agents on Deterministic Simulations of Real Websites
https://github.com/agiresearch/asb
공식 코드Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
https://github.com/agiresearch/openagi
공식 코드https://github.com/agiresearch/openp5
공식 코드https://github.com/ahjolsenbics/evanyseg
공식 코드Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images
https://github.com/ahmadmughees/sh17dataset
공식 코드https://github.com/ahmedest61/multires-netvlad
공식 코드MultiRes-NetVLAD: Augmenting Place Recognition Training with Low-Resolution Imagery
https://github.com/ahmedmagdiosman/clevr-xai
공식 코드Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
https://github.com/ahmedmagdiosman/simply-clevr-dataset
공식 코드Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
https://github.com/ahwang16/grounded-intuition-gpt-vision
공식 코드Grounded Intuition of GPT-Vision's Abilities with Scientific Images
https://github.com/ahxt/fair_fairness_benchmark
공식 코드FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
https://github.com/ai-agents-2030/SPA-Bench
공식 코드SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation
https://github.com/ai-lab-y/nn_cryptanalytic_extraction
공식 코드Hard-Label Cryptanalytic Extraction of Neural Network Models
https://github.com/ai4bharat/cia
공식 코드Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs
https://github.com/ai4ce/SNAC
공식 코드Simultaneous Navigation and Construction Benchmarking Environments
https://github.com/ai4finance-foundation/fingpt
공식 코드FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets
https://github.com/ai4healthuol/mds-ed
공식 코드https://github.com/ai4healthuol/ppg-ood-generalization
공식 코드https://github.com/ai4life-group/openxai
공식 코드OpenXAI: Towards a Transparent Evaluation of Model Explanations
https://github.com/aicenter/ridesharing_darp_instances
공식 코드Large-scale Ridesharing DARP Instances Based on Real Travel Demand
https://github.com/aichipdesign/chipgptv
공식 코드Natural language is not enough: Benchmarking multi-modal generative AI for Verilog generation
https://github.com/aiconslab/roodmri
공식 코드https://github.com/aida-ugent/NRL4LP
공식 코드Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?
https://github.com/aidos-lab/MANTRA
공식 코드https://github.com/aidos-lab/rings
공식 코드No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets
https://github.com/aifeg/benchgpt
공식 코드BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models
https://github.com/aifeg/benchlmm
공식 코드BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.