SOTAVerified

Transfer Learning

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.

( Image credit: Subodh Malgonde )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5651–5675 of 10307 papers

TitleStatusHype
MUCS@Text-LT-EDI@ACL 2022: Detecting Sign of Depression from Social Media Text using Supervised Learning Approach—0
CogTaskonomy: Cognitively Inspired Task Taxonomy Is Beneficial to Transfer Learning in NLPCode0
Amazon Alexa AI’s System for IWSLT 2022 Offline Speech Translation Shared Task—0
Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach—0
Conversational AI for Positive-sum Retailing under Falsehood ControlCode0
Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues—0
Shallow Parsing for Nepal Bhasa Complement Clauses—0
S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method—0
Cross-lingual Semantic Role Labelling with the ValPaL Database Knowledge—0
SSN@LT-EDI-ACL2022: Transfer Learning using BERT for Detecting Signs of Depression from Social Media Texts—0
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out TrainingCode0
A Checkpoint on Multilingual Misogyny Identification—0
On Target Representation in Continuous-output Neural Machine Translation—0
Challenges in including extra-linguistic context in pre-trained language models—0
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural textCode0
Towards Detecting Political Bias in Hindi News Articles—0
When does CLIP generalize better than unimodal models? When judging human-centric concepts—0
Uncertainty Regularized Multi-Task Learning—0
Transfer Learning and Prediction Consistency for Detecting Offensive Spans of Text—0
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model—0
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual TransferCode0
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series ForecastCode0
Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation—0
Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
Show:102550
← PrevPage 227 of 413Next →
All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified