Posts
1604
Following
138
Followers
882
I'm a bit of an eclectic mess 🙂 I've been a programmer, journalist, editor, TV producer, and a few other things.

I'm currently working on my second novel which is complete, but is in the edit stage. I wrote my first novel over 20 years ago but then didn't write much till now.

I post about #Coding, #Flutter, #Writing, #Movies and #TV. I'll also talk about #Technology, #Gadgets, #MachineLearning, #DeepLearning and a few other things as the fancy strikes ...

Lived in: 🇱🇰🇸🇦🇺🇸🇳🇿🇸🇬🇲🇾🇦🇪🇫🇷🇪🇸🇵🇹🇶🇦🇨🇦
f

Fahim Farook

I really miss having @qikipedia posting facts over here. For a week or so, they did and then they’ve gone back to only posting on Twitter 😞

So I guess I’ll have to repeat their facts since I do enjoy them … but then there is no quoting posts and I have to resort to screenshots .. Ah, well 🙂
There is a termite city underne…
1
0
3
f

Fahim Farook

"UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models. (arXiv:2302.04867v1 [cs.LG])" — A unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports arbitrary order as a byproduct.

Paper: http://arxiv.org/abs/2302.04867
Code: https://github.com/wl-zhao/UniPC

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
The main idea of UniPC. We prov…
0
1
0
f

Fahim Farook

"Trading Information between Latents in Hierarchical Variational Autoencoders. (arXiv:2302.04855v1 [stat.ML])" — A generalization of VAEs to application domains beyond generative modeling (e.g., representation learning, clustering, or lossy data compression) by introducing an objective function that allows practitioners to trade off between the information content ("bit rate") of the latent representation and the distortion of reconstructed data.

Paper: http://arxiv.org/abs/2302.04855

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
Left: trade-off between perform…
0
1
0
f

Fahim Farook

"Robot Synesthesia: A Sound and Emotion Guided AI Painter. (arXiv:2302.04850v1 [cs.CV])" — An approach for using sound and speech to guide a robotic painting process by encoding the simulated paintings and input sounds into the same latent space.

Paper: http://arxiv.org/abs/2302.04850
Code: https://github.com/pschaldenbrand/Frida

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
Our robot painting sound guidan…
0
1
1
f

Fahim Farook

"Is This Loss Informative? Speeding Up Textual Inversion with Deterministic Objective Evaluation. (arXiv:2302.04841v1 [cs.CV])" — A study of the training dynamics of textual inversion, with the aim of speeding it up.

Paper: http://arxiv.org/abs/2302.04841

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
A summary of our key findings: …
0
1
0
f

Fahim Farook

"Better Diffusion Models Further Improve Adversarial Training. (arXiv:2302.04638v1 [cs.CV])" — Tries to answer the question "Can better diffusion models further improve adversarial training?" empirically.

Paper: http://arxiv.org/abs/2302.04638
Code: https://github.com/wzekai99/DM-Improves-AT

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
A brief summary comparison of t…
0
1
0
f

Fahim Farook

"Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples. (arXiv:2302.04578v1 [cs.CV])" — Exploration into creating an image that is similar to another image for human vision, but unrecognizable for diffusion models to investigate the possibility of creating images that hinder diffusion models from extracting their features.

Paper: http://arxiv.org/abs/2302.04578

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
Comparison of workflows for adv…
0
1
0
f

Fahim Farook

"Efficient Attention via Control Variates. (arXiv:2302.04542v1 [cs.LG])" — A look at control variates to show that Random-Feature-based Attention (RFA) can be decomposed into a sum of multiple control variate estimators for each element in the sequence.

Paper: http://arxiv.org/abs/2302.04542

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
Left and middle: empirical memo…
0
1
0
f

Fahim Farook

"Feature Likelihood Score: Evaluating Generalization of Generative Models Using Samples. (arXiv:2302.04440v1 [cs.LG])" — A parametric sample-based score that uses density estimation to quantitatively measure the quality/diversity of generated samples of deep generative models while taking overfitting into account.

Paper: http://arxiv.org/abs/2302.04440
Code: https://github.com/marcojira/fls

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
FLS for a generative model that…
0
1
0
f

Fahim Farook

"Q-Diffusion: Quantizing Diffusion Models. (arXiv:2302.04304v1 [cs.CV])" — A solution to the slowness in diffusion models to generate images by compressing the noise estimation network to accelerate the generation process using post-training quantization (PTQ).

Paper: http://arxiv.org/abs/2302.04304

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
Stable Diffusion 512 ×512 text-…
0
1
0
f

Fahim Farook

Thought I’d put the new #ChatGPT plugin through its paces (I’m not linking to it, given the results, I think I’d be doing everybody a disservice linking to it …) and installed it. The very first Google query I had was for something coding related that I was stuck on …

The ChatGPT answer? Looks convincing but total BS 😛

PSA: Kids, do not rely on ChatGPT and always verify the info you receive from strangers (and ChatGPT or its ilk).

#MisinformationEngine #ChatGPT #Search
If you're using a `LazyVGrid` i…
1
0
1
f

Fahim Farook

"What do we learn? Debunking the Myth of Unsupervised Outlier Detection. (arXiv:2206.03698v2 [cs.CV] UPDATED)" — An investigation into what Auto-Encoders (AE) actually learn when they are posed to solve two different tasks and challenging the assumption that AEs are likely to be even better at reconstructing some types of Out-of-Distribution (OoD) samples.

Paper: http://arxiv.org/abs/2206.03698

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
Unsupervised outlier detection …
0
2
0
f

Fahim Farook

"Improved Vector Quantized Diffusion Models. (arXiv:2205.16007v2 [cs.CV] UPDATED)" — Two techniques to improve the sample quality of VQ-Diffusion. 1) Explore classifier-free guidance sampling for discrete denoising diffusion models to propose a more general and effective implementation of classifier-free guidance. 2) Use a high-quality inference strategy to alleviate the joint distribution issue in VQ-Diffusion.

Paper: http://arxiv.org/abs/2205.16007
Code: https://github.com/microsoft/vq-diffusion
Demos: https://huggingface.co/spaces/patrickvonplaten/vq-vs-stable-diffusion
https://huggingface.co/spaces/williamberman/vq-diffusion

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
In-the-wild text-to-image synth…
0
1
0
f

Fahim Farook

“Mining Effective Features Using Quantum Entropy for Humor Recognition” — A paper which looks at dividing a joke into two components — the setup and the punchline — and examining the possible semantics and relationships between these components.

Paper: https://arxiv.org/abs/2302.03716

#NewPaper #Language #Computation

<<Find this useful? Please boost so that others can benefit too 🙂>>
A humor and non-humor example c…
0
1
0
f

Fahim Farook

"Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models. (arXiv:2302.03900v1 [cs.CV])" — A neural pipeline for generating a coherent storybook from the plain text of a story by leveraging a combination of a pre-trained Large Language Model and a text-guided Latent Diffusion Model to generate coherent images.

Paper: http://arxiv.org/abs/2302.03900

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
Zero-shot generation example of…
0
1
0
f

Fahim Farook

"Neural Artistic Style Transfer with Conditional Adversaria. (arXiv:2302.03875v1 [cs.CV])" — Two methods that step toward a style image independent neural style transfer model which could generate semantically accurate generated images for any content, style image input pair.

Paper: http://arxiv.org/abs/2302.03875
Code: https://github.com/nipdep/STGAN

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
The generated image (c), which …
0
1
1
f

Fahim Farook

"TetCNN: Convolutional Neural Networks on Tetrahedral Meshes. (arXiv:2302.03830v1 [cs.CV])" — A novel interpretable graph Convolutional Neural Network (CNN) framework for tetrahedral mesh structures, inspired by ChebyNet.

Paper: http://arxiv.org/abs/2302.03830

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
TetCNN architecture for the cla…
0
2
1
f

Fahim Farook

"How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control. (arXiv:2302.03791v1 [stat.ML])" — A focus on image-to-image regression tasks to arrive at a generalization of the Risk-Controlling Prediction Sets (RCPS) procedure, which allows to provide: i) entrywise calibrated intervals for future samples of any diffusion model, ii) control a certain notion of risk with respect to a ground truth image with minimal mean interval length.

Paper: http://arxiv.org/abs/2302.03791

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
Example images
0
0
0
f

Fahim Farook

"Understanding Why ViT Trains Badly on Small Datasets: An Intuitive Perspective. (arXiv:2302.03751v1 [cs.CV])" — A visual intuition to help understand why ViT has a significantly lower evaluation accuracy when trained on small datasets when compared to ResNet-18 with a similar number of parameters.

Paper: http://arxiv.org/abs/2302.03751
Code: https://github.com/BoyuanJackChen/Visualize-Transformer-ResNet18

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
Visualization for ViT on CIFAR-…
0
1
0
f

Fahim Farook

"Towards causally linking architectural parametrizations to algorithmic bias in neural networks. (arXiv:2302.03750v1 [cs.CV])" — A causal framework for linking an architectural hyperparameter to algorithmic bias to study effect of hyperparameters on inducing biases.

Paper: http://arxiv.org/abs/2302.03750

#AI #CV #NewPaper #DeepLearning #MachineLearning

<<Find this useful? Please boost so that others can benefit too 🙂>>
The deep learning model develop…
0
1
0
Show older
About The Farooks

Terms of Service

This is a placeholder, overwrite this by putting a file at 

$STATIC_DIR/static/terms-of-service.html

See the Static Directory docs for more info.