alinlab/L2T-ww

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Created over 5 years ago
Updated about 4 years ago

alinlab/L2T-ww

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Learning What and Where to Transfer (ICML 2019)

Learning What and Where to Transfer (ICML 2019)

Learning What and Where to Transfer (ICML 2019) https://arxiv.org/abs/1905.05901

Requirements

python>=3.6
pytorch>=1.0
torchvision
cuda>=9.0

Note. The reported results in our paper were obtained in the old-version pytorch (pytorch=1.0, cuda=9.0). We recently executed again the experiment commands as described below using the recent version (pytorch=1.6.0, torchvision=0.7.0, cuda=10.1), and obtained similar results as reported in the paper.

Prepare Datasets

You can download CUB-200 and Stanford Dogs datasets

CUB-200: from http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
Stanford Dogs: http://vision.stanford.edu/aditya86/ImageNetDogs/

You need to run the below pre-processing script for DataLoader.

python cub200.py /data/CUB_200_2011
python dog.py /data/dog

Train L2T-ww

You can train L2T-ww models with the same settings in our paper.

python train_l2t_ww.py --dataset cub200 --datasplit cub200 --dataroot /data/CUB_200_2011
python train_l2t_ww.py --dataset dog --datasplit dog --dataroot /data/dog
python train_l2t_ww.py --dataset cifar100 --datasplit cifar100 --dataroot /data/ --experiment logs/cifar100_0/ --source-path logs --source-model resnet32 --source-domain tinyimagenet-200 --target-model vgg9_bn --pairs 4-0,4-1,4-2,4-3,4-4,9-0,9-1,9-2,9-3,9-4,14-0,14-1,14-2,14-3,14-4 --batchSize 128
python train_l2t_ww.py --dataset stl10 --datasplit stl10 --dataroot /data/ --experiment logs/stl10_0/ --source-path logs --source-model resnet32 --source-domain tinyimagenet-200 --target-model vgg9_bn --pairs 4-0,4-1,4-2,4-3,4-4,9-0,9-1,9-2,9-3,9-4,14-0,14-1,14-2,14-3,14-4 --batchSize 128

CSI

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)

Confident_classifier

Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018

cs-kd

Regularizing Class-wise Predictions via Self-knowledge Distillation (CVPR 2020)

M2m

Code for the paper "M2m: Imbalanced Classification via Major-to-minor Translation" (CVPR 2020)

LfF

Learning from Failure: Training Debiased Classifier from Biased Classifier (NeurIPS 2020)

ifseg

IFSeg: Image-free Semantic Segmentation via Vision-Language Model (CVPR 2023)

SelfPatch

consistency-adversarial

Consistency Regularization for Adversarial Robustness (AAAI 2022)

PsCo

Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning (ICLR 2023)

MASKER

MASKER: Masked Keyword Regularization for Reliable Text Classification (AAAI 2021)

object-aware-contrastive

Object-aware Contrastive Learning for Debiased Scene Representation (NeurIPS 2021)

s-clip

S-CLIP: Semi-supervised Vision-Language Pre-training using Few Specialist Captions

lookahead_pruning

Lookahead: A Far-sighted Alternative of Magnitude-based Pruning (ICLR 2020)

temporal-selfsupervision

BAR

The repository for the official Biased Action Recognition (BAR) dataset for the paper Learning from Failure: Training Debiased Classifier from Biased Classifier (NeurIPS 2020) by Junhyun Nam et al.

HOMER

Official implementation of Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs (ICLR 2024).

oreo

b2t

Bias-to-Text: Debiasing Unknown Visual Biases through Language Interpretation

OpenCoS

MetaMAE

Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder (NeurIPS 2023)

SURF

lime-cam-pytorch

Pytorch implemenation of LIME-CAM

OAMixer

OAMixer: Object-aware Mixing Layer for Vision Transformers (CVPRW 2022)

smoothing-catrs

Code for the paper "Confidence-aware Training of Smoothed Classifiers for Certified Robustness" (AAAI 2023)

ml-papers-classification