https://github.com/marcelomatheusgauy/pretrained_audio_neural_networks_emotion_recognition
공식 코드Pretrained audio neural networks for Speech emotion recognition in Portuguese
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/marcelosancinetti/epa-gop-pykaldi
공식 코드A transfer learning based approach for pronunciation scoring
https://github.com/marciahon29/Ryerson_MRP
공식 코드Towards Alzheimer's Disease Classification through Transfer Learning
https://github.com/marco-roberti/char-data-to-text-gen
공식 코드Copy mechanism and tailored training for character-based data-to-text generation
https://github.com/marcocominelli/csi-vae
공식 코드Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer
https://github.com/marcuxu/psmg
공식 코드PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization
https://github.com/marian-nmt/marian
공식 코드Approaching Neural Chinese Word Segmentation as a Low-Resource Machine Translation Task
https://github.com/marianocabezas/challenges2018
공식 코드Survival prediction using ensemble tumor segmentation and transfer learning
https://github.com/maris205/genome_book
공식 코드https://github.com/marko-orescanin-nps/vi-pann
공식 코드https://github.com/marko-ruman/RL-Correspondence-Learner
공식 코드https://github.com/markywg/transagent
공식 코드TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration
https://github.com/marrlab/dinobloom
공식 코드DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
https://github.com/martinbarry59/gateonmnist
공식 코드Context selectivity with dynamic availability enables lifelong continual learning
https://github.com/martinwimpff/eeg-otta
공식 코드Calibration-free online test-time adaptation for electroencephalography motor imagery decoding
https://github.com/masakhane-io/masakhane-ner
공식 코드https://github.com/masakhane-io/masakhane-ner/tree/main/MasakhaNER2.0
공식 코드MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
https://github.com/masilab/3dux-net
공식 코드https://github.com/masilab/repux-net
공식 코드Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation
https://github.com/master-cai/voloc
공식 코드VOLoc: Visual Place Recognition by Querying Compressed Lidar Map
https://github.com/masterai-eam/darwin
공식 코드DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
https://github.com/maszhongming/paraknowtransfer
공식 코드Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective
https://github.com/materialsvirtuallab/maml
공식 코드Discovery of 2D materials using Transformer Network based Generative Design
https://github.com/mathiaskraus/MarketBasket
공식 코드Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching
https://github.com/mathlee/gelenet
공식 코드Salient Object Detection in Optical Remote Sensing Images Driven by Transformer
https://github.com/mathwrx/BA-Net/blob/main/encoding/models/BANet.py
공식 코드Boundary-aware Context Neural Network for Medical Image Segmentation
https://github.com/matinamehdizadeh/Breast-Cancer-Detection
공식 코드https://github.com/matnlp/cmedmrc
공식 코드https://github.com/matteo-bastico/mi-seg
공식 코드https://github.com/matthewcym/sflm
공식 코드Revisiting Self-Training for Few-Shot Learning of Language Model
https://github.com/matthias-k/DeepGaze
공식 코드DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling
https://github.com/mattiacampana/transfer-learning-covid-19
공식 코드Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio Data
https://github.com/matyasbohacek/uwb-sl-wild
공식 코드Learning from What is Already Out There: Few-shot Sign Language Recognition with Online Dictionaries
https://github.com/maximecb/gym-miniworld
공식 코드Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning
https://github.com/maximilianigl/rl-msol
공식 코드https://github.com/maxlchen/Speech-PreFinetuning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.