Gopal Krishna (gokriznastic)

gokriznastic

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Company:Northeastern University

Location:Boston, MA

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Gopal Krishna's starred repositories

cs-video-courses

List of Computer Science courses with video lectures.

rich

Rich is a Python library for rich text and beautiful formatting in the terminal.

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RobustVideoMatting

Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

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imaginAIry

Pythonic AI generation of images and videos

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cog

Containers for machine learning

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accelerate

🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

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SlowFast

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

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ConvNeXt

Code release for ConvNeXt model

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DiT

Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"

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monodepth2

[ICCV 2019] Monocular depth estimation from a single image

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deep-rl-class

This repo contains the syllabus of the Hugging Face Deep Reinforcement Learning Course.

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TensorRT

PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT

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lion-pytorch

🦁 Lion, new optimizer discovered by Google Brain using genetic algorithms that is purportedly better than Adam(w), in Pytorch

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Awesome-Image-Inpainting

A curated list of image inpainting and video inpainting papers and resources

DenseDepth

High Quality Monocular Depth Estimation via Transfer Learning

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arxiv.py

Python wrapper for the arXiv API

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PyGCL

PyGCL: A PyTorch Library for Graph Contrastive Learning

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LIA

[ICLR 22] Latent Image Animator: Learning to Animate Images via Latent Space Navigation

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ic_gan

Official repository for the paper "Instance-Conditioned GAN" by Arantxa Casanova, Marlene Careil, Jakob Verbeek, Michał Drożdżal, Adriana Romero-Soriano.

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eft

visualization code for 3D human body annotation by EFT (Exemplar Fine-tuning)

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eye-in-the-sky

Satellite Image Classification using semantic segmentation methods in deep learning

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PASS

The PASS dataset: pretrained models and how to get the data

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xview-yolov3

xView 2018 Object Detection Challenge: YOLOv3 Training and Inference.

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hyperseg

HyperSeg - Official PyTorch Implementation

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OFASys

OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models

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FD-GAN

FD-GAN: Generative adversarial Networks with Fusion-discriminator for Single Image Dehazing(AAAI'20)

deep_imitative_models

Reimplementation (currently partial) of Deep Imitative Models paper, ICLR '20

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3D-Vision-and-Touch

When told to understand the shape of a new object, the most instinctual approach is to pick it up and inspect it with your hand and eyes in tandem. Here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to fusing vision and touch, which leverages advances in graph convolutional networks. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single-modality baselines, especially when the object is occluded by the hand touching it; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) reconstruction quality boosts with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood.

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Implicit-Q-Learning

PyTorch implementation of the implicit Q-learning algorithm (IQL)

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