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acai-iclr-2019

Anonymized code for ICLR 2019 submission "Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer"

custom_wordle

custom wordle

exposure_fusion

Exposure fusion is a technique that creates a single image with optimal detail from a set of multi-exposed images. As developed by Tom Mertens et al., the proposed algorithm computes relevant quality measures; Contrast, Saturation, and Well-Exposedness. These measures are then combined to create a weight map used to blend each of the multi-exposed images to a single image with best exposure.

iclr2020-openreviewdata

Script that crawls meta data from ICLR OpenReview webpage. Tutorials on installing and using Selenium and ChromeDriver on Ubuntu.

influence_function

once-for-all

[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment

pairwise_comparison

ranking from pairwise comparison (MLE, RC, Borda)

pytorch-image-models

PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MixNet, MobileNet-V3/V2, MNASNet, Single-Path NAS, FBNet, and more

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

singan

Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"

slimmable_networks

Slimmable Neural Networks, ICLR 2019