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Domain Adaptation for Time Series Under Feature and Label Shifts
An open source implementation of CLIP.
Just Select Twice: Leveraging Low Quality Data to Improve Data Selection
Croissant is a high-level format for machine learning datasets that brings together four rich layers.
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
State-of-the-Art Text Embeddings
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing (ACL2023 Findings)
Benchmark your model on out-of-distribution datasets with carefully collected human comparison data (NeurIPS 2021 Oral)
This is the official implmentation of Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data (IJCAI 2022 Long Oral)
Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification on HAM10000 dataset largescale data.
This implements training of popular model architectures, such as AlexNet, ResNet and VGG on the ImageNet dataset(Now we supported alexnet, vgg, resnet, squeezenet, densenet)
pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation
Data Shapley: Equitable Valuation of Data for Machine Learning
Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.
OpenDataVal: a Unified Benchmark for Data Valuation in Python (NeurIPS 2023)
This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.
This is an official repository for "LAVA: Data Valuation without Pre-Specified Learning Algorithms" (ICLR2023).
Optimal transport tools implemented with the JAX framework, to get differentiable, parallel and jit-able computations.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Corruption and Perturbation Robustness (ICLR 2019)
J-D. Benamou, G. Carlier, M. Cuturi, L. Nenna, G. Peyré. Iterative Bregman Projections for Regularized Transportation Problems. SIAM Journal on Scientific Computing, 37(2), pp. A1111–A1138, 2015.
Contains the code relative to the paper Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning https://arxiv.org/abs/2002.08276