https://github.com/pritamqu/SSL-ECG
공식 코드Self-supervised ECG Representation Learning for Emotion Recognition
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/pritamqu/XKD
공식 코드XKD: Cross-modal Knowledge Distillation with Domain Alignment for Video Representation Learning
https://github.com/prompteus/audio-captioning
공식 코드A Whisper transformer for audio captioning trained with synthetic captions and transfer learning
https://github.com/prs-eth/film-ensemble
공식 코드FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
https://github.com/prs-eth/marigold
공식 코드Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis
https://github.com/psunlpgroup/xsemplr
공식 코드XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations
https://github.com/pth1993/muvitanet
공식 코드Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning
https://github.com/puchapu/utep
공식 코드Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling
https://github.com/pujari-rajkumar/rl-guided-multitask-learning
공식 코드Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection
https://github.com/pulp-bio/bmi-odcl
공식 코드https://github.com/pumpkin-co/mtl-lora
공식 코드https://github.com/puzzle-ai/unpuzzle
공식 코드https://github.com/pvnieo/vader
공식 코드Generalizable Local Feature Pre-training for Deformable Shape Analysis
https://github.com/pwr-inf/transfer-learning-approach-to-bicycle-sharing-systems-station-location-planning-using-openstreetmap
공식 코드https://github.com/pxaris/ccml
공식 코드From West to East: Who can understand the music of the others better?
https://github.com/pxiangwu/MotionNet
공식 코드Joint Perception and Prediction for Autonomous Driving: A Survey
https://github.com/pxuab/emo2vec_wassa_paper
공식 코드Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
https://github.com/pygda-team/pygda
공식 코드https://github.com/pykale/pykale
공식 코드PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
https://github.com/pykt-team/pykt-toolkit
공식 코드https://github.com/pysentimiento/robertuito
공식 코드RoBERTuito: a pre-trained language model for social media text in Spanish
https://github.com/pytorch/fairseq
공식 코드https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text
공식 코드https://github.com/pzelasko/kaldi
공식 코드Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition
https://github.com/q-future/q-sit
공식 코드https://github.com/qcraftai/distill-bev
공식 코드DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation
https://github.com/qianc62/MGTC
공식 코드An Approach for Process Model Extraction By Multi-Grained Text Classification
https://github.com/qiancheng0/creator
공식 코드CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models
https://github.com/qianlima-lab/time-series-ptms
공식 코드https://github.com/qiaolian9/pruner
공식 코드Pruner: A Speculative Exploration Mechanism to Accelerate Tensor Program Tuning
https://github.com/qingkaikong/3dfno_gcs
공식 코드Multi-fidelity Fourier Neural Operator for Fast Modeling of Large-Scale Geological Carbon Storage
https://github.com/qingkaikong/damaged_building_detection
공식 코드Detecting Damage Building Using Real-time Crowdsourced Images and Transfer Learning
https://github.com/qingyu-qc/bioner_gerbera
공식 코드Augmenting Biomedical Named Entity Recognition with General-domain Resources
https://github.com/qiuzh20/emoe
공식 코드https://github.com/qiwang067/CoWorld
공식 코드Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning
https://github.com/qiyu3816/MTFNN-CO
공식 코드A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.