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IronMan's Projects

maxent-arl icon maxent-arl

PyTorch Implementation of CVPR'19 (oral) - Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach

mcl-okd icon mcl-okd

Multi-view contrastive learning for online knowledge distillation (MCL-OKD)

mdenas icon mdenas

Multinomial Distribution Learning for Effective Neural Architecture Search

mdeq icon mdeq

[NeurIPS'20] Multiscale Deep Equilibrium Models

memnn icon memnn

Memory Networks implementations

memory-optimal-direct-convolutions icon memory-optimal-direct-convolutions

Code for reproducing work of ICML 2019 paper: Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications

meta-weight-net icon meta-weight-net

NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).

metad2a icon metad2a

Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)

metaquant icon metaquant

Codes for Accepted Paper : "MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization" in NeurIPS 2019

mfas icon mfas

Implementation of CVPR 2019 paper "Mfas: Multimodal fusion architecture search"

mgd icon mgd

Matching Guided Distillation (ECCV 2020)

milenas icon milenas

MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. Published in CVPR 2020

miniargs icon miniargs

A wrapper of argparse which I can remember the APIs.

mixmatch-pytorch-1 icon mixmatch-pytorch-1

Pytorch Implementation of the paper MixMatch: A Holistic Approach to Semi-Supervised Learning (https://arxiv.org/pdf/1905.02249.pdf)

mixnet-pytorch icon mixnet-pytorch

A PyTorch implementation of MixNet: Mixed Depthwise Convolutional Kernels

mlp-nasbench-101 icon mlp-nasbench-101

this implements a search agent that uses MLP to predict the top candidates for searching over NASBench-101

mmaml-classification icon mmaml-classification

An official PyTorch implementation of “Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation” (NeurIPS 2019) by Risto Vuorio*, Shao-Hua Sun*, Hexiang Hu, and Joseph J. Lim

mmcv icon mmcv

Open MMLab Computer Vision Foundation

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