https://github.com/imsy-dkfz/htc
공식 코드Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis
Papers with Code 아카이브
Transfer Learning
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- 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/ina-foss/InaGVAD
공식 코드https://github.com/indooradventurer/vittransferlearningforartclassification
공식 코드https://github.com/indy-lab/ProtoTransfer
공식 코드Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
https://github.com/inigo-jauregi/t3l
공식 코드T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification
https://github.com/injadlu/VCR
공식 코드Rethinking Visual Content Refinement in Low-Shot CLIP Adaptation
https://github.com/ink-usc/crosstaskmoe
공식 코드Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Experts
https://github.com/inm-6/alpaca_use_case
공식 코드https://github.com/innovator-zero/MTDP_Lib
공식 코드https://github.com/insilicomedicine/DILBERT
공식 코드Drug and Disease Interpretation Learning with Biomedical Entity Representation Transformer
https://github.com/intellabs/matsciml
공식 코드MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
https://github.com/intellabs/model-compression-research-package
공식 코드https://github.com/intelligolabs/mtl-split
공식 코드MTL-Split: Multi-Task Learning for Edge Devices using Split Computing
https://github.com/internlm/internlm-math
공식 코드InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning
https://github.com/intlyc/mto-platform
공식 코드MToP: A MATLAB Optimization Platform for Evolutionary Multitasking
https://github.com/ioanabica/htce-learners
공식 코드Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation
https://github.com/iobt-vistec/min2net
공식 코드MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
https://github.com/ipsitmantri/DiTASK
공식 코드DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations
https://github.com/ireis/PRF
공식 코드Probabilistic Random Forest: A machine learning algorithm for noisy datasets
https://github.com/irfannafiz/recme
공식 코드https://github.com/irip-buaa/a-review-for-remote-sensing-vision-language-models
공식 코드A Survey on Remote Sensing Foundation Models: From Vision to Multimodality
https://github.com/is5882/uu-tax
공식 코드https://github.com/isaaccorley/resize-is-all-you-need
공식 코드Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters
https://github.com/isadrtdinov/ens-for-transfer
공식 코드To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning
https://github.com/isakfalk/atomistic_transfer_mekrr
공식 코드Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
https://github.com/isbrycee/gem
공식 코드GEM: Boost Simple Network for Glass Surface Segmentation via Vision Foundation Models
https://github.com/isbrycee/gem-glass-segmentor
공식 코드https://github.com/isdling/predicttcl
공식 코드Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive Learning
https://github.com/isi-nlp/astrapop
공식 코드https://github.com/isi-nlp/nlcodec
공식 코드Many-to-English Machine Translation Tools, Data, and Pretrained Models
https://github.com/islam-nassar/lava
공식 코드https://github.com/ismail31416/luminet
공식 코드LumiNet: The Bright Side of Perceptual Knowledge Distillation
https://github.com/ispamm/MMD-Bayesian-Neural-Network
공식 코드Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
https://github.com/ispc-lab/lead
공식 코드LEAD: Learning Decomposition for Source-free Universal Domain Adaptation
https://github.com/issararab/pevolm
공식 코드PEvoLM: Protein Sequence Evolutionary Information Language Model
https://github.com/ist-daslab/cram
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.