https://github.com/steve-zeyu-zhang/MedDet
공식 코드MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detection
Papers with Code 아카이브
Knowledge Distillation
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Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/steven-tel/cen-hdr
공식 코드CEN-HDR: Computationally Efficient neural Network for real-time High Dynamic Range imaging
https://github.com/stevetsui/ida-det
공식 코드IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors
https://github.com/stogiannidis/srbench
공식 코드Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
https://github.com/stonybrooknlp/appworld
공식 코드AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents
https://github.com/stream-bench/stream-bench
공식 코드StreamBench: Towards Benchmarking Continuous Improvement of Language Agents
https://github.com/sunanhe/mkt
공식 코드Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer
https://github.com/sunblaze-ucb/llm-api-audit
공식 코드Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs
https://github.com/sunblaze-ucb/verina
공식 코드https://github.com/suncerock/EAsT-music-classification
공식 코드https://github.com/sundw2014/DCM
공식 코드Knowledge Transfer via Dense Cross-Layer Mutual-Distillation
https://github.com/sungl001/spaced-kd
공식 코드https://github.com/sungwon-han/dualfair
공식 코드https://github.com/sungwon-han/fedx
공식 코드FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
https://github.com/sunnweiwei/co-bench
공식 코드CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial Optimization
https://github.com/sunnyhaze/iml-vit
공식 코드IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer
https://github.com/sunnysoni97/single_image_fl
공식 코드https://github.com/sunshangquan/logit-standardardization-kd
공식 코드https://github.com/suntongtongtong/benchmark-robustness-text-image-compose-retrieval
공식 코드https://github.com/sunyoungIT/UDCA-Net
공식 코드https://github.com/sunyueue/RDD
공식 코드https://github.com/super-SSS/DCKD
공식 코드Dynamic Contrastive Knowledge Distillation for Efficient Image Restoration
https://github.com/superallen13/GCondenser
공식 코드https://github.com/superhero-7/altdiffuson
공식 코드https://github.com/superjamessyx/robustness_benchmark
공식 코드Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology
https://github.com/supertext/evaluation_deepl_supertext
공식 코드A comparison of translation performance between DeepL and Supertext
https://github.com/surgan12/nastyattacks
공식 코드https://github.com/suscom-lab/wattsonai
공식 코드WattsOnAI: Measuring, Analyzing, and Visualizing Energy and Carbon Footprint of AI Workloads
https://github.com/sutd-visual-computing-group/LS-KD-compatibility
공식 코드Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?
https://github.com/swag2198/microbiome-symbolic-regression
공식 코드Interpreting Microbiome Relative Abundance Data Using Symbolic Regression
https://github.com/swaggy-tn/efficientvlm
공식 코드https://github.com/swagshaw/rainbow-keywords
공식 코드Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting
https://github.com/swarupbehera/FastAST
공식 코드https://github.com/swathi-shree-narashiman/alphazip
공식 코드https://github.com/sweetpeach/hummingbird
공식 코드Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica
https://github.com/sweety-dm/interference-aware-deep-q-learning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.