Papers with Code 아카이브
Meta-Learning
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Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.
<span style="color:grey; opacity: 0.6">( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xinyadu/rgqa
공식 코드Retrieval-Augmented Generative Question Answering for Event Argument Extraction
https://github.com/xinyu-laura/mmuda
공식 코드https://github.com/xinyuzc/PABBO
공식 코드https://github.com/xinzheli1217/learning-to-self-train
공식 코드Learning to Self-Train for Semi-Supervised Few-Shot Classification
https://github.com/xiongxiaoxu/metagad
공식 코드MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection
https://github.com/xiyangl3/robust_dp
공식 코드https://github.com/xiye17/interpcalib
공식 코드Can Explanations Be Useful for Calibrating Black Box Models?
https://github.com/xjtushujun/meta-weight-net
공식 코드Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
https://github.com/xjtushujun/slem-theory
공식 코드Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
https://github.com/xlhex/gal_syntex
공식 코드https://github.com/xliucs/MetaPhys
공식 코드MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement
https://github.com/xmed-lab/scan
공식 코드Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease Classification
https://github.com/xuechaochen/p3p-mae
공식 코드P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked Autoencoders
https://github.com/xuesongwang/global-convolutional-neural-processes
공식 코드https://github.com/xuhuali-mxj/im-dcl
공식 코드https://github.com/xuhuali-mxj/stepspt
공식 코드https://github.com/xulabs/aitom
공식 코드https://github.com/xunzhaoyu/experience-based-saea
공식 코드Experience-Based Evolutionary Algorithms for Expensive Optimization
https://github.com/xxlifelover/meta-gradient-augmentation
공식 코드Improving Generalization in Meta-Learning via Meta-Gradient Augmentation
https://github.com/xxyqwq/metascript
공식 코드MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial Networks
https://github.com/xylgroup/mlmc
공식 코드Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation
https://github.com/xyvivian/hyper
공식 코드Fast Unsupervised Deep Outlier Model Selection with Hypernetworks
https://github.com/xyxy880/metasd
공식 코드https://github.com/y1ny/WordDeletion
공식 코드Active Use of Latent Constituency Representation in both Humans and Large Language Models
https://github.com/yair-schiff/ipnp
공식 코드Semi-Parametric Inducing Point Networks and Neural Processes
https://github.com/yan-scnu/prompted_dynamic_detection
공식 코드Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4
https://github.com/yananwu0510/ml-sgm
공식 코드Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition
https://github.com/yandachen/in-context-tuning
공식 코드https://github.com/yang-yk/NP-RepMet
공식 코드https://github.com/yangarbiter/nearest-category-generalization
공식 코드https://github.com/yangjinluan/dam
공식 코드Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace
https://github.com/yangpuhai/Granularity-in-DST
공식 코드https://github.com/yangxu351/synthetic_xview_airplanes
공식 코드SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems
https://github.com/yangyangyang127/ape
공식 코드Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
https://github.com/yangyangyang127/seg-nn
공식 코드No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.