Forwarded from Gradient Dude
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
A framework that learns meaningful directions in GANs' latent space using unsupervised contrastive learning. Instead of discovering fixed directions such as in previous work, this method can discover non-linear directions in pretrained StyleGAN2 and BigGAN models. The discovered directions may be used for image manipulation.
Authors use the differences caused by an edit operation on the feature activations to optimize the identifiability of each direction. The edit operations are modeled by several separate neural nets
π Paper
π Code (next week)
#paper_tldr #cv #gan
A framework that learns meaningful directions in GANs' latent space using unsupervised contrastive learning. Instead of discovering fixed directions such as in previous work, this method can discover non-linear directions in pretrained StyleGAN2 and BigGAN models. The discovered directions may be used for image manipulation.
Authors use the differences caused by an edit operation on the feature activations to optimize the identifiability of each direction. The edit operations are modeled by several separate neural nets
β_i(z) and learning. Given a latent code z and its generated image x = G(z), we seek to find edit operations β_i(z) such that the image x' = G(β_i(z)) has semantically meaningful changes over x while still preserving the identity of x.π Paper
π Code (next week)
#paper_tldr #cv #gan
Forwarded from Towards NLPπΊπ¦
Conversational AI Reading List
List of interesting papers as well as some link to the lectures from Conversational AI course for Columbia University:
https://docs.google.com/spreadsheets/u/0/d/1nSKcnM5r9x82BdyPgn-obN1sRUlLC7zZ082a0132Igk/htmlview#gid=1523499517
List of interesting papers as well as some link to the lectures from Conversational AI course for Columbia University:
https://docs.google.com/spreadsheets/u/0/d/1nSKcnM5r9x82BdyPgn-obN1sRUlLC7zZ082a0132Igk/htmlview#gid=1523499517
Forwarded from Sysadmin Tools πΊπ¦
Advanced Database Systems
#database #db #sql #nosql
This course is a comprehensive study of the internals of modern database management systems. It will cover the core concepts and fundamentals of the components that are used in both high-performance transaction processing systems (OLTP) and large-scale analytical systems (OLAP).YouTube Playlist
#database #db #sql #nosql
CMU 15-721
Schedule - CMU 15-721 :: Advanced Database Systems (Spring 2020)
Course schedule with slides, lecture notes, and videos.
Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains
This is an interesting paper about learning and combining representations of object shape and appearance from the different domains (for example, dogs and cars). This allows to create a model, which borrows different properties from each domain and generates images, which don't exist in a single domain.
The main idea is the following:
- use FineGAN as a base model;
- represent object appearance with a differentiable histogram of visual features;
- optimize the generator so that images with different shapes but similar appearances produce similar histograms;
Paper: https://openreview.net/forum?id=M88oFvqp_9
Project link: https://utkarshojha.github.io/inter-domain-gan/
Code will be available here: https://github.com/utkarshojha/inter-domain-gan
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-furrycars
#cv #gan #deeplearning #contrastivelearning
This is an interesting paper about learning and combining representations of object shape and appearance from the different domains (for example, dogs and cars). This allows to create a model, which borrows different properties from each domain and generates images, which don't exist in a single domain.
The main idea is the following:
- use FineGAN as a base model;
- represent object appearance with a differentiable histogram of visual features;
- optimize the generator so that images with different shapes but similar appearances produce similar histograms;
Paper: https://openreview.net/forum?id=M88oFvqp_9
Project link: https://utkarshojha.github.io/inter-domain-gan/
Code will be available here: https://github.com/utkarshojha/inter-domain-gan
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-furrycars
#cv #gan #deeplearning #contrastivelearning
OpenReview
Generating Furry Cars: Disentangling Object Shape and Appearance...
We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e.g., dogs and cars). The goal is to learn a generative model that...
Today @ π11:00 CET.
βοΈ π₯π«π· Parisian Data Breakfast will be held online π! See you soon at
https://spatial.chat/s/DataBreakfast
βοΈ π₯π«π· Parisian Data Breakfast will be held online π! See you soon at
https://spatial.chat/s/DataBreakfast
app.spatial.chat
SpatialChat
Virtual space platform to help remote teams collaborate.
Forwarded from Self Supervised Boy
Self-supervision paper from arxiv for histopathology CV.
Authors draw inspiration from the process of how histopathologists tend to review the images, and how those images are stored. Histopathology images are multiscale slices of enormous size (tens of thousands pixels by one side), and area experts constantly move through different levels of magnification to keep in mind both fine and coarse structures of the tissue.
Therefore, in this paper the loss is proposed to capture relation between different magnification levels. Authors propose to train network to order concentric patches by their magnification level. They organise it as the classification task β network to predict id of the order permutation instead of predicting order itself.
Also, authors proposed specific architecture for this task and appended self-training procedure, as it was shown to boost results even after pre-training.
All this allows them to reach quality increase even in high-data regime.
My description of the architecture and loss expanded here.
Source of the work here.
Authors draw inspiration from the process of how histopathologists tend to review the images, and how those images are stored. Histopathology images are multiscale slices of enormous size (tens of thousands pixels by one side), and area experts constantly move through different levels of magnification to keep in mind both fine and coarse structures of the tissue.
Therefore, in this paper the loss is proposed to capture relation between different magnification levels. Authors propose to train network to order concentric patches by their magnification level. They organise it as the classification task β network to predict id of the order permutation instead of predicting order itself.
Also, authors proposed specific architecture for this task and appended self-training procedure, as it was shown to boost results even after pre-training.
All this allows them to reach quality increase even in high-data regime.
My description of the architecture and loss expanded here.
Source of the work here.
swanky-pleasure-bcf on Notion
Self-supervised driven consistency training for annotation efficient histopathology image analysis | Notion
In this paper authors gain insight for the new loss from the way histopathologists work with images. Since the enormous scale of the images for histopathological research it is stored in pyramid-like structure with different zoom level, so researches tendβ¦
π3
Unsupervised 3D Neural Rendering of Minecraft Worlds
Work on unsupervised neural rendering framework for generating photorealistic images of Minecraft (or any large 3D block worlds).
Why this is cool: this is a step towards better graphics for games.
Project Page: https://nvlabs.github.io/GANcraft/
YouTube: https://www.youtube.com/watch?v=1Hky092CGFQ&t=2s
#GAN #Nvidia #Minecraft
Work on unsupervised neural rendering framework for generating photorealistic images of Minecraft (or any large 3D block worlds).
Why this is cool: this is a step towards better graphics for games.
Project Page: https://nvlabs.github.io/GANcraft/
YouTube: https://www.youtube.com/watch?v=1Hky092CGFQ&t=2s
#GAN #Nvidia #Minecraft
Forwarded from Graph Machine Learning
Awesome graph repos
Collections of methods and papers for specific graph topics.
Graph-based Deep Learning Literature β Links to Conference Publications and the top 10 most-cited publications, Related workshops, Surveys / Literature Reviews / Books in graph-based deep learning.
awesome-graph-classification β A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations.
Awesome-Graph-Neural-Networks β A collection of resources related with graph neural networks..
awesome-graph β A curated list of resources for graph databases and graph computing tools
awesome-knowledge-graph β A curated list of Knowledge Graph related learning materials, databases, tools and other resources.
awesome-knowledge-graph β A curated list of awesome knowledge graph tutorials, projects and communities.
Awesome-GNN-Recommendation β graph mining for recommender systems.
awesome-graph-attack-papers β links to works about adversarial attacks and defenses on graph data or GNNs.
Graph-Adversarial-Learning β Attack-related papers, Defense-related papers, Robustness Certification papers, etc., ranging from 2017 to 2021.
awesome-self-supervised-gnn β Papers about self-supervised learning on GNNs.
awesome-self-supervised-learning-for-graphs β A curated list for awesome self-supervised graph representation learning resources.
Awesome-Graph-Contrastive-Learning β Collection of resources related with Graph Contrastive Learning.
Collections of methods and papers for specific graph topics.
Graph-based Deep Learning Literature β Links to Conference Publications and the top 10 most-cited publications, Related workshops, Surveys / Literature Reviews / Books in graph-based deep learning.
awesome-graph-classification β A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations.
Awesome-Graph-Neural-Networks β A collection of resources related with graph neural networks..
awesome-graph β A curated list of resources for graph databases and graph computing tools
awesome-knowledge-graph β A curated list of Knowledge Graph related learning materials, databases, tools and other resources.
awesome-knowledge-graph β A curated list of awesome knowledge graph tutorials, projects and communities.
Awesome-GNN-Recommendation β graph mining for recommender systems.
awesome-graph-attack-papers β links to works about adversarial attacks and defenses on graph data or GNNs.
Graph-Adversarial-Learning β Attack-related papers, Defense-related papers, Robustness Certification papers, etc., ranging from 2017 to 2021.
awesome-self-supervised-gnn β Papers about self-supervised learning on GNNs.
awesome-self-supervised-learning-for-graphs β A curated list for awesome self-supervised graph representation learning resources.
Awesome-Graph-Contrastive-Learning β Collection of resources related with Graph Contrastive Learning.
GitHub
GitHub - naganandy/graph-based-deep-learning-literature: links to conference publications in graph-based deep learning
links to conference publications in graph-based deep learning - naganandy/graph-based-deep-learning-literature
π1
Data Science by ODS.ai π¦
Starting -1 Data Science Breakfast as an audio chat
Starting 0 Data Breakfast as an audio chat in this channel in 15 minutes.
This is an informal online event where you can discuss anything related to Data Science (even vaguely related).
This is an informal online event where you can discuss anything related to Data Science (even vaguely related).
Forwarded from Gradient Dude
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Researchers from Berkeley rolled out VideoGPT - a transformer that generates videos.
The results are not super "WOW", but the architecture is quite simple and now it can be a starting point for all future work in this direction. As you know, GPT-3 for text generation was also not built right away. So let's will wait for method acceleration and quality improvement.
πPaper
βοΈCode
πProject page
πDemo
The results are not super "WOW", but the architecture is quite simple and now it can be a starting point for all future work in this direction. As you know, GPT-3 for text generation was also not built right away. So let's will wait for method acceleration and quality improvement.
πPaper
βοΈCode
πProject page
πDemo
For almost 5 years channel picture beared arbitrary picture found in google and now we updated it with a proper new channel logo generated by neural network. Do you like it?
