https://github.com/tsun/ssrt
공식 코드Safe Self-Refinement for Transformer-based Domain Adaptation
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/ttslr/strengthnet
공식 코드StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis
https://github.com/tu-rbo/concarne
공식 코드https://github.com/tueimage/lung-nodule-msc-2018
공식 코드Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
https://github.com/tufts-ai-robotics-group/automaton-guided-cl
공식 코드Automaton-Guided Curriculum Generation for Reinforcement Learning Agents
https://github.com/tufts-ml/bdl-transfer-learning
공식 코드Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported
https://github.com/tufts-ml/data-emphasized-ELBo
공식 코드https://github.com/tufts-ml/supcontrast
공식 코드SINCERE: Supervised Information Noise-Contrastive Estimation REvisited
https://github.com/tum-aimed/octa-seg
공식 코드https://github.com/tunazislam/covid_fb_ad_minimalsup
공식 코드Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision
https://github.com/tunib-ai/parallelformers
공식 코드AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing
https://github.com/turing-online-safety-codebase/cheap_learning
공식 코드Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data
https://github.com/tushaarkataria/histopathology-domain-specific-pretraining
공식 코드https://github.com/tushar-semwal/TransferLearning_CNN_TextClassification
공식 코드https://github.com/tushar117/Transformer-Models-for-Text-Coherence-Assessment
공식 코드https://github.com/tushartalukder/bengali-asr-model-using-wav2vec2
공식 코드An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer Learning
https://github.com/tuvuumass/task-transferability
공식 코드https://github.com/twanvl/adrem
공식 코드https://github.com/twinkle0331/lgtm
공식 코드Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation
https://github.com/twytock/cell_reprogramming_by_transfer_learning
공식 코드Cell reprogramming design by transfer learning of functional transcriptional networks
https://github.com/tylerachang/multilingual-geometry
공식 코드https://github.com/tylersco/adapted_deep_embeddings
공식 코드Adapted Deep Embeddings: A Synthesis of Methods for $k$-Shot Inductive Transfer Learning
https://github.com/ubc-nlp/l2asr
공식 코드https://github.com/ubcdingxin/soap-kd
공식 코드https://github.com/uber/ludwig
공식 코드https://github.com/ucf-sst-lab/aicity2024cvprw
공식 코드Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis
https://github.com/uchidalab/time-series-transferability
공식 코드https://github.com/ucl-candi/freehand
공식 코드https://github.com/uclanlp/robust-xlt
공식 코드Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training
https://github.com/ucsb-seclab/BullseyePoison
공식 코드Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability
https://github.com/uddipanmukherjee/cardiovascular_audio_classification
공식 코드A Visual Domain Transfer Learning Approach for Heartbeat Sound Classification
https://github.com/uds-lsv/afro-maft
공식 코드Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning
https://github.com/uds-lsv/emoji-transfer
공식 코드https://github.com/uds-lsv/intermediate-task-selection
공식 코드Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning
https://github.com/uds-lsv/semantically_enriched_awes
공식 코드Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings
https://github.com/uds-lsv/transfer-distant-transformer-african
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.