https://github.com/bingo-g/qmamba
공식 코드QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
Papers with Code 아카이브
Transfer Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 6
- 보존된 평가 기록
- 10
Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.
<span style="color:grey; opacity: 0.6">( Image credit: Subodh Malgonde )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bingrao/ctin
공식 코드CTIN: Robust Contextual Transformer Network for Inertial Navigation
https://github.com/binuxliu/nocplace
공식 코드NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer
https://github.com/bioIntelligence-Lab/qumi
공식 코드https://github.com/biomedia-mbzuai/fissionfusion
공식 코드FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis
https://github.com/biomedia-mira/atmil
공식 코드Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification
https://github.com/biomedia-mira/chexploration
공식 코드Algorithmic encoding of protected characteristics in image-based models for disease detection
https://github.com/bioscan-ml/dataset
공식 코드https://github.com/birlrobotics/rlkit_autotune
공식 코드Hyperparameter Auto-tuning in Self-Supervised Robotic Learning
https://github.com/bit-da/eada
공식 코드Active Learning for Domain Adaptation: An Energy-Based Approach
https://github.com/bit-da/lsg
공식 코드https://github.com/bit-da/scda
공식 코드https://github.com/bit-ml/cshift
공식 코드Self-Supervised Learning in Multi-Task Graphs through Iterative Consensus Shift
https://github.com/bit-xjy/rl-based-transferable-ems
공식 코드https://github.com/bita-github/MRP-FER
공식 코드https://github.com/blackzxy/logah
공식 코드LoGAH: Predicting 774-Million-Parameter Transformers using Graph HyperNetworks with 1/100 Parameters
https://github.com/bloomberg/dataless-model-merging
공식 코드Dataless Knowledge Fusion by Merging Weights of Language Models
https://github.com/bojesomo/Traffic4Cast2020-DeepSedanionNetwork
공식 코드https://github.com/bonaventuredossou/afrivec
공식 코드AfriVEC: Word Embedding Models for African Languages. Case Study of Fon and Nobiin
https://github.com/bonheml/vae_learning_dynamics
공식 코드https://github.com/boschresearch/MetaBO
공식 코드Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
https://github.com/boschresearch/mocl-naacl-2024
공식 코드Rehearsal-Free Modular and Compositional Continual Learning for Language Models
https://github.com/boschresearch/predicting_sets_of_sources
공식 코드To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning
https://github.com/boschresearch/transfergpbo
공식 코드Transfer Learning with Gaussian Processes for Bayesian Optimization
https://github.com/boschresearch/transfersafesequentiallearning
공식 코드Global Safe Sequential Learning via Efficient Knowledge Transfer
https://github.com/boscoj2008/adapterem
공식 코드https://github.com/bostoncake/c-visdit
공식 코드Confidence-based Visual Dispersal for Few-shot Unsupervised Domain Adaptation
https://github.com/bowang-lab/ecg-fm
공식 코드https://github.com/bowang-lab/medsam
공식 코드Segment Anything in Medical Images and Videos: Benchmark and Deployment
https://github.com/bowenc0221/Decoupled-Classification-Refinement
공식 코드Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
https://github.com/bplank/DaNplus
공식 코드DaN+: Danish Nested Named Entities and Lexical Normalization
https://github.com/bplank/semi-supervised-baselines
공식 코드Strong Baselines for Neural Semi-supervised Learning under Domain Shift
https://github.com/braemt/attentive-multi-task-deep-reinforcement-learning
공식 코드https://github.com/brain-cog-lab/transfer-for-dvs
공식 코드An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain
https://github.com/brainsharks-fyp17/sinhala-text-simplification-dataset-and-evaluation
공식 코드https://github.com/bramgrooten/automatic-noise-filtering
공식 코드Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.