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Found 24 papers, 8 papers with code
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks.
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
In this paper we introduce a generative parametric model capable of producing high quality samples of natural images.
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss.
Unsupervised Learning of Disentangled Representations from Video
We present a new model DrNET that learns disentangled image representations from video.
Stochastic Video Generation with a Learned Prior
Sample generations are both varied and sharp, even many frames into the future, and compare favorably to those from existing approaches.
Ranked #3 on
Video Prediction
on KTH
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning
In hierarchical reinforcement learning a major challenge is determining appropriate low-level policies.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias
Facial analysis models are increasingly used in applications that have serious impacts on people's lives, ranging from authentication to surveillance tracking.
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing
Although essential to revealing biased performance, well intentioned attempts at algorithmic auditing can have effects that may harm the very populations these measures are meant to protect.
Computers and Society
Diversity and Inclusion Metrics in Subset Selection
The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives.
Social Biases in NLP Models as Barriers for Persons with Disabilities
Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models.
Characterising Bias in Compressed Models
However, overall accuracy hides disproportionately high errors on a small subset of examples; we call this subset Compression Identified Exemplars (CIE).
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure
In this paper, we introduce a rigorous framework for dataset development transparency which supports decision-making and accountability.
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Datasets have played a foundational role in the advancement of machine learning research.
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development
Specifically, we examine what dataset documentation communicates about the underlying values of vision data and the larger practices and goals of computer vision as a field.
AI and the Everything in the Whole Wide World Benchmark
There is a tendency across different subfields in AI to valorize a small collection of influential benchmarks.
Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research
Despite the foundational role of benchmarking practices in this field, relatively little attention has been paid to the dynamics of benchmark dataset use and reuse, within or across machine learning subcommunities.
Ethics and Creativity in Computer Vision
This paper offers a retrospective of what we learnt from organizing the workshop *Ethical Considerations in Creative applications of Computer Vision* at CVPR 2021 conference and, prior to that, a series of workshops on *Computer Vision for Fashion, Art and Design* at ECCV 2018, ICCV 2019, and CVPR 2020.
Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation
Human annotations play a crucial role in machine learning (ML) research and development.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding.
Ranked #17 on
Text-to-Image Generation
on MS COCO
(using extra training data)
CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation
Human annotated data plays a crucial role in machine learning (ML) research and development.
City-Wide Perceptions of Neighbourhood Quality using Street View Images
Researchers demonstrated the efficacy of crowd-sourcing perception ratings of image pairs across 56 cities and training a model to predict perceptions from street-view images.
AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia
This paper presents a community-centered study of cultural limitations of text-to-image (T2I) models in the South Asian context.
SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata
The unstructured nature of data used in foundation model development is a challenge to systematic analyses for making data use and documentation decisions.
