https://github.com/gicheonkang/sglkt-visdial
공식 코드Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
Papers with Code 아카이브
Transfer Learning
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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/gift-surg/HighRes3DNet
공식 코드https://github.com/gimpong/MM23-MISSRec
공식 코드https://github.com/gingl/cmpa
공식 코드https://github.com/gingsmith/fmtl
공식 코드https://github.com/ginnm/proteinpretraining
공식 코드https://github.com/girishvn/bigsmall
공식 코드https://github.com/glam-imperial/nsl-ser
공식 코드Knowledge Transfer For On-Device Speech Emotion Recognition with Neural Structured Learning
https://github.com/glamor-usc/climb
공식 코드CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks
https://github.com/gloewing/smtl
공식 코드Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives
https://github.com/glycanml/glycanml
공식 코드GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning
https://github.com/gmum/icicle
공식 코드https://github.com/goelshivam1210/RAPid-Learn
공식 코드RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments
https://github.com/gohyojun15/ANT_diffusion
공식 코드https://github.com/gokulkarthik/deformable-detr
공식 코드An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers
https://github.com/gokulkarthik/detr
공식 코드An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers
https://github.com/goldblum/AdversarialQuerying
공식 코드Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
https://github.com/gomes-lab/H-CLMP
공식 코드Materials Representation and Transfer Learning for Multi-Property Prediction
https://github.com/google-research-datasets/Textual-Entailment-New-Protocols
공식 코드New Protocols and Negative Results for Textual Entailment Data Collection
https://github.com/google-research-datasets/ToTTo
공식 코드https://github.com/google-research-datasets/screen_qa
공식 코드ScreenQA: Large-Scale Question-Answer Pairs over Mobile App Screenshots
https://github.com/google-research-datasets/simulated-dialogue
공식 코드https://github.com/google-research/ALBERT
공식 코드ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/google-research/adapter-bert
공식 코드https://github.com/google-research/big_transfer
공식 코드https://github.com/google-research/big_vision
공식 코드A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision
https://github.com/google-research/chirp
공식 코드Global birdsong embeddings enable superior transfer learning for bioacoustic classification
https://github.com/google-research/crosslingual-knowledge-barriers
공식 코드Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models
https://github.com/google-research/federated
공식 코드Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
https://github.com/google-research/federated/tree/master/personalization_benchmark
공식 코드Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
https://github.com/google-research/generative_transfer
공식 코드https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/adaptive_low_rank
공식 코드AdaRank: Disagreement Based Module Rank Prediction for Low-rank Adaptation
https://github.com/google-research/google-research/tree/master/dp_transfer
공식 코드https://github.com/google-research/google-research/tree/master/dselect_k_moe
공식 코드https://github.com/google-research/google-research/tree/master/fm4tlp
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.