Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration.

Last update: Jul 28, 2022

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Overview

Information Gain Filtration

Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration. The provided Jupyter Notebook gives a simple demostration into the use of IGF during language model finetuning. Data for this demonstration is available on Figshare here.

If you use this method in your published work, please cite the ACL paper that describes this method here.

Owner

GitHub Repository

Code release for "Making a Bird AI Expert Work for You and Me".

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Code/data of the paper "Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction" (BMVC2021)

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13 Nov 07, 2022

Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration.

Related tags

Overview

Information Gain Filtration

Owner

Code release for "Making a Bird AI Expert Work for You and Me".

Attempt at implementation of a simple GAN using Keras

PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, wav2lip, picture repair, image editing, photo2cartoon, image style transfer, and so on.

Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling

Constrained Logistic Regression - How to apply specific constraints to logistic regression's coefficients

Revisiting, benchmarking, and refining Heterogeneous Graph Neural Networks.

RL and distillation in CARLA using a factorized world model

Editing a Conditional Radiance Field

Make differentially private training of transformers easy for everyone

CVPR2021 Workshop - HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization.

FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization

Code/data of the paper "Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction" (BMVC2021)

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

This is a JAX implementation of Neural Radiance Fields for learning purposes.

Semi-Supervised Signed Clustering Graph Neural Network (and Implementation of Some Spectral Methods)

The toolkit to generate auto labeled datasets

A Pytorch Implementation of ClariNet

This is 2nd term discrete maths project done by UCU students that uses backtracking to solve various problems.

[ICCV 2021 (oral)] Planar Surface Reconstruction from Sparse Views

Position detection system of mobile robot in the warehouse enviroment