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mldl's Projects

conv_arithmetic icon conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

convmae icon convmae

ConvMAE: Masked Convolution Meets Masked Autoencoders

convnetjs icon convnetjs

Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.

convnext-tf icon convnext-tf

Includes PyTorch -> Keras model porting code for ConvNeXt family of models with fine-tuning and inference notebooks.

corenlp icon corenlp

Stanford CoreNLP: A Java suite of core NLP tools.

cosformer icon cosformer

Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention

cosformer-pytorch icon cosformer-pytorch

Unofficial PyTorch implementation of the paper "cosFormer: Rethinking Softmax In Attention".

cotta icon cotta

[CVPR 2022] CoTTA Code for our paper Continual Test-Time Domain Adaptation

cream icon cream

[NeurIPS'20] Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search

crfasrnn icon crfasrnn

This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/

crnn icon crnn

Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition.

cs-gnn icon cs-gnn

Measuring and Improving the Use of Graph Information in Graph Neural Networks

csra icon csra

Official code of ICCV2021 paper "Residual Attention: A Simple but Effective Method for Multi-Label Recognition"

cst icon cst

Code release for "Cycle Self-Training for Domain Adaptation" (NeurIPS 2021)

curl icon curl

CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning

cutout icon cutout

2.56%, 15.20%, 1.30% on CIFAR10, CIFAR100, and SVHN https://arxiv.org/abs/1708.04552

cxxnet icon cxxnet

move forward to https://github.com/dmlc/mxnet

cycle-consistent-vae icon cycle-consistent-vae

This repository contains the code for the paper: Disentangling Factors of Variation with Cycle-Consistent Variational Auto-encoders (https://arxiv.org/abs/1804.10469), which was accepted at ECCV 2018.

d-vae icon d-vae

D-VAE: A Variational Autoencoder for Directed Acyclic Graphs, NeurIPS 2019

d2conv3d icon d2conv3d

D²Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos

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