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Data-free Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/seohongpark/ogbench
공식 코드https://github.com/seonokkim/3dyoga90
공식 코드3DYoga90: A Hierarchical Video Dataset for Yoga Pose Understanding
https://github.com/serval-uni-lu/tabularbench
공식 코드TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
https://github.com/servicenow/agentlab
공식 코드https://github.com/servicenow/broad-openood
공식 코드Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection
https://github.com/servicenow/browsergym
공식 코드https://github.com/seu-vipgroup/fg-bmk
공식 코드Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation
https://github.com/seung-b/fl-psg
공식 코드Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation
https://github.com/sfofgalaxy/jalmbench
공식 코드JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models
https://github.com/shadowkiller33/contrast-instruction
공식 코드https://github.com/shailja-thakur/vgen
공식 코드Benchmarking Large Language Models for Automated Verilog RTL Code Generation
https://github.com/shams-mehdi/aib9_openmm
공식 코드A survey of probabilistic generative frameworks for molecular simulations
https://github.com/shanay-mehta/anomaly-benchmarking
공식 코드Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study
https://github.com/shanghai-digital-brain-laboratory/db-football
공식 코드An Empirical Study on Google Research Football Multi-agent Scenarios
https://github.com/sharc-lab/hlsfactory
공식 코드HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond
https://github.com/sharif-apu/lsd-tfformer
공식 코드Illuminating Darkness: Enhancing Real-world Low-light Scenes with Smartphone Images
https://github.com/shark-nlp/cab
공식 코드CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
https://github.com/sharpiless/rgal
공식 코드Relation-Guided Adversarial Learning for Data-free Knowledge Transfer
https://github.com/shashankskagnihotri/adv-corrected-ddcat-cospgd
공식 코드CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
https://github.com/shashankskagnihotri/benchmarking_reliability_generalization
공식 코드SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification
https://github.com/shashankskagnihotri/benchmarking_robustness
공식 코드Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions?
https://github.com/shashankskagnihotri/cospgd
공식 코드CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
https://github.com/shashidharjavaji/RC_BENCH
공식 코드https://github.com/shaunak27/grain-clip
공식 코드Grounding Descriptions in Images informs Zero-Shot Visual Recognition
https://github.com/shawnan-whu/choice
공식 코드CHOICE: Benchmarking the Remote Sensing Capabilities of Large Vision-Language Models
https://github.com/shehzaadzd/MINERVA
공식 코드Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks
https://github.com/shenyanghuang/tgb
공식 코드TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
https://github.com/shijianjian/ImmersePro
공식 코드ImmersePro: End-to-End Stereo Video Synthesis Via Implicit Disparity Learning
https://github.com/shijieS/SST
공식 코드https://github.com/shijieS/people-counting-dataset
공식 코드Benchmark data and method for real-time people counting in cluttered scenes using depth sensors
https://github.com/shilin-lu/vine
공식 코드Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances
https://github.com/shimazaki/pnlss
공식 코드A projected nonlinear state-space model for forecasting time series signals
https://github.com/shimhz/a_dcf
공식 코드a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verification
https://github.com/shiqiyu/opengait
공식 코드LidarGait: Benchmarking 3D Gait Recognition with Point Clouds
https://github.com/shivangi-aneja/COSMOS
공식 코드COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.