Matt Smith's repositories

dinov2

PyTorch code and models for the DINOv2 self-supervised learning method.

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AFN

Moire Removal via Attentive Fractal Network, CVPRW 2020

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custom_ops

Deformable Convolution Networks(deformable conv2D TF_implementation)

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digital-garden-jekyll-template

Start your own digital garden using this Jekyll template 🌱

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fourier-deconvolution-network

Code for ICCV 2017 paper "Learning to Push the Limits of Efficient FFT-based Image Deconvolution" (Jakob Kruse, Carsten Rother, and Uwe Schmidt)

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ijepa

Official codebase for I-JEPA, the Image-based Joint-Embedding Predictive Architecture. First outlined in the CVPR paper, "Self-supervised learning from images with a joint-embedding predictive architecture."

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memory-efficient-attention-pytorch

Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"

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models

Models and examples built with TensorFlow

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muse-maskgit-pytorch

Implementation of Muse: Text-to-Image Generation via Masked Generative Transformers, in Pytorch

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odl

Operator Discretization Library https://odlgroup.github.io/odl/

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perceiver-io

A PyTorch implementation of DeepMind's Perceiver IO with PyTorch Lightning scripts for distributed training

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phase_retrieval

Implement Phase Retrieval

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resume

Compact and Minimal LaTeX Resume Template

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score_sde

Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

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score_sde_pytorch

PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

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SimMIM

This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

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SlowFast

PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

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spectral-repr-cnns

Implementation of Rippel, Snoek, and Adams 2015 (https://arxiv.org/pdf/1506.03767.pdf)

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torchscale

Transformers at any scale

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vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

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