BAIR

JULY 9, 2023

Much prior theoretical work has focused on analytical similarities in their loss functions, but our experiments suggest a different unifying principle: SSL methods all learn embeddings one dimension at a time, iteratively adding new dimensions in order of salience.

Neural Network 197

Neural Network Deep Learning Algorithm 197

ODSC - Open Data Science

APRIL 3, 2023

Are you curious about explainability methods like saliency maps but feel lost about where to begin? Are you fascinated by the power of Question Answering (QA) models but find yourself intimidated by technical challenges? Do you yearn to compare different QA models but dread the time-consuming process of setting them up?

Neural Network 117

Neural Network Natural Language Processing NLP Computational Linguistics 117

Artificial Corner

JUNE 11, 2023

After partitioning the input image into tokens, a salience map is calculated for each token to determine their importance. One strategy for computational efficiency is reducing the number of tokens fed into the vision transformer encoder.

Categorization 52

Categorization Artificial Intelligence Artificial Intelligence 52

Marktechpost

JULY 24, 2023

Saliency map visualization: The authors used Grad-CAM to visualize where TALL-Swin was paying attention to the deepfake faces. They found that TALL-Swin achieved state-of-the-art results. They found that TALL-Swin was able to capture method-specific artifacts and focus on important regions, such as the face and mouth regions.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Heartbeat

NOVEMBER 27, 2023

Methods such as attention mechanisms, saliency maps, and gradient-based visualization provide insights into which regions of the image contribute most to the model's decision. Interpreting and explaining the decisions of deep learning models is crucial for building trust and confidence in their application.

Deep Learning 59

Deep Learning Convolutional Neural Networks Explainability Neural Network 59

Unite.AI

OCTOBER 30, 2023

The framework then deploys a skin beautification & a saliency detection model to obtain a clean & clear face training image. Once the framework detects the face, it crops the input image using a predefined specific ratio that focuses solely on the facial region.

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AI AI Generative AI Algorithm 147

BAIR

JULY 10, 2023

Much prior theoretical work has focused on analytical similarities in their loss functions, but our experiments suggest a different unifying principle: SSL methods all learn embeddings one dimension at a time, iteratively adding new dimensions in order of salience.

Neural Network 100

Neural Network Deep Learning Algorithm 100

Google Research AI blog

MARCH 16, 2023

Globally responsible AI centering community experts Our team also recognizes the saliency of understanding the socio-technical context globally. This is a research challenge for the field of ML in general and one that is central to our team.

Responsible AI 111

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Mlearning.ai

FEBRUARY 2, 2023

You can also use techniques such as saliency maps, LIME, and SHAP to understand how the model is making its predictions. Metrics for ML — Not looking at the model  — Explaining a ML model can help in understanding how it works and makes predictions.

Machine Learning 52

Machine Learning ML Categorization Data Analysis 52

Jay Alammar

JANUARY 18, 2021

These visualizations are all created using Ecco , the open-source package we're releasing In the first part of this series, Interfaces for Explaining Transformer Language Models , we showcased interactive interfaces for input saliency and neuron activations. Examining attention or inner-layer saliency could reveal clues as to the reason.

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BERT Explainability 72

Google Research AI blog

DECEMBER 7, 2022

A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification (see blog post ) Jasmijn Bastings , Sebastian Ebert , Polina Zablotskaia , Anders Sandholm , Katja Filippova Intriguing Properties of Compression on Multilingual Models Kelechi Ogueji*, Orevaoghene Ahia, Gbemileke A.

Natural Language Processing 52

Natural Language Processing NLP BERT Large Language Models 52

Google Research AI blog

JUNE 13, 2023

Modeling human attention (the result of which is often called a saliency model) has therefore been of interest across the fields of neuroscience, psychology, human-computer interaction (HCI) and computer vision. Our method employs a state-of-the-art deep saliency model. memory, comprehension, action).

Computer Vision 78

Computer Vision Neural Network Software Engineer Machine Learning 78

Google Research AI blog

DECEMBER 6, 2022

Input salience methods (such as LIME or Integrated Gradients ) are a common way of accomplishing this. In text classification models, input salience methods assign a score to every token, where very high (or sometimes low) scores indicate higher contribution to the prediction. So, which one should be used for discovering shortcuts?

BERT 52

BERT Neural Network Software Engineer NLP 52

Mlearning.ai

FEBRUARY 6, 2023

Even if attention does accurately show word importance, is the word importance (AKA input saliency) truly the best way to conduct these types of explanations? Can a human look at a saliency map and understand how the highlighted words signal AI-generated content? I don’t think so.

Explainable AI 52

Explainable AI Explainability ChatGPT Chatbots 52

Jay Alammar

DECEMBER 16, 2020

Interfaces for exploring transformer language models by looking at input saliency and neuron activation. In it, we present explorables and visualizations aiding the intuition of: Input Saliency methods that score input tokens importance to generating a token. input saliency is a method that explains individual predictions.

Explainability 52

Explainability Auto-complete Auto-classification Neural Network 52

Google Research AI blog

APRIL 18, 2023

Experimental results We evaluate the performance of our algorithm in two different domains: real-world location check-in data and image saliency data. We also experiment on the Salicon image saliency dataset (SALICON ). This dataset is a collection of saliency annotations on the Microsoft Common Objects in Context image database.

Algorithm 98

Algorithm Computer Vision Software Engineer Deep Learning 98

Google Research AI blog

MAY 18, 2023

Another Explorable, “ Searching for Unintended Biases with Saliency ”, shows how spurious correlations can lead to unintended biases — and how techniques such as saliency maps can detect some biases in datasets, with the caveat that it can be difficult to see bias when it’s more subtle and sporadic in a training set.

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The Stanford AI Lab Blog

DECEMBER 6, 2021

Wainwright, Emma Brunskill Contact : zanette@berkeley.edu Links: Paper Keywords : offline rl, mirror descent, bellman closure A Topological Perspective on Causal Inference Authors : Duligur Ibeling, Thomas Icard Contact : icard@stanford.edu Links: Paper Keywords : causal inference, topological learning theory Adversarial Training Helps Transfer Learning (..)

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Neural Network Deep Learning Machine Learning AI 40

Towards AI

JULY 21, 2023

LIT supports local explanations — including salience maps, attention, and rich visualizations of model predictions — as well as aggregate analysis — including metrics, embedding spaces, and flexible slicing — and allows users to seamlessly hop between them to test local […] I tend to view LIT as an ML demo on steroids for prototyping.

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NLP BERT Deep Learning Natural Language Processing 90

Probably Approximately a Scientific Blog

MARCH 8, 2016

In our text classification task, it learns the salience of each word in the vocabulary for each topic. There is a broad choice of algorithms, some may perform better than others. Roughly, they all try to do the same thing: t he multi-class classifier learns a weight for each feature and each label.

Algorithm 52

Algorithm Natural Language Processing Machine Learning Python 52

Viso.ai

APRIL 25, 2024

XAI offers several techniques to identify potential biases within deep learning models: Saliency Maps : These highlight regions in an image that are most influential in the model’s decision. Explainable AI improves the transparency of those models making them more trustworthy. A focus on irrelevant elements might flag potential bias.

Computer Vision 52

Computer Vision Categorization Algorithm Explainability 52

Google Research AI blog

JUNE 18, 2023

Xingyi Zhou , Anurag Arnab , Chen Sun , Cordelia Schmid Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur Peng Dai, Yinda Zhang , Xin Yu, Xiaoyang Lyu, Xiaojuan Qi IFSeg: Image-Free Semantic Segmentation via Vision-Language Model Sukmin Yun, Seong Park, Paul Hongsuck Seo , Jinwoo Shin Learning from Unique Perspectives: User-Aware Saliency (..)

Computer Vision 116

Computer Vision Robotics Large Language Models Categorization 116

Viso.ai

FEBRUARY 12, 2024

Working of LIME method Apart from these, other prominent Explainable AI methods include ICE plots, Tree surrogates, Counterfactual Explanations, saliency maps, and rule-based models. Interpret the explainer: Now, you can interpret the explainer model with any method like feature importance, PDP, etc.to

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Explainable AI Explainability Deep Learning Neural Network 52

Lexalytics

APRIL 5, 2021

Radev (2004) “ LexRank: graph-based lexical centrality as salience in text summarization ” Thomas L. Lowe (2004) “ Distinctive Image Features from Scale-Invariant Keypoints ” Bo Pang and Lillian Lee (2004) “ A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts ” Gunes Erkan and Dragomir R.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

ML Review

MAY 3, 2018

Figure 5 : Saliency map of “Please” Note from source : Saliency shows the places where LipNet has learned to attend, i.e. the phonologically important regions. The pictured transcription is given by greedy CTC decoding.

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52

Google Research AI blog

JANUARY 24, 2023

We also published five new interactive explorable visualizations that introduce key ideas and guidance to the research community, including how to use saliency to detect unintended biases in ML models , and how federated learning can be used to collaboratively train a model with data from multiple users without any raw data leaving their devices.

Responsible AI 99

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