Papers Odd One Out
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Computer Vision Models Show Human-Like Sensitivity to Geometric and Topological Concepts
With the rapid improvement of machine learning (ML) models, cognitive scientists are increasingly asking about their alignment with how humans think. Here, we ask this question for computer vision models and human sensit…
Odd One OutSensitivityO1O: Grouping of Known Classes to Identify Unknown Objects as Odd-One-Out
Object detection methods trained on a fixed set of known classes struggle to detect objects of unknown classes in the open-world setting. Current fixes involve adding approximate supervision with pseudo-labels correspond…
object-detectionObject DetectionOdd One OutOdd-One-Out: Anomaly Detection by Comparing with Neighbors
This paper introduces a novel anomaly detection (AD) problem that focuses on identifying `odd-looking' objects relative to the other instances in a given scene. In contrast to the traditional AD benchmarks, anomalies in …
8kAnomaly DetectionOdd One OutAn Analysis of Human Alignment of Latent Diffusion Models
Diffusion models, trained on large amounts of data, showed remarkable performance for image synthesis. They have high error consistency with humans and low texture bias when used for classification. Furthermore, prior wo…
Image GenerationOdd One OutTripletTowards Generative Abstract Reasoning: Completing Raven's Progressive Matrix via Rule Abstraction and Selection
Endowing machines with abstract reasoning ability has been a long-term research topic in artificial intelligence. Raven's Progressive Matrix (RPM) is widely used to probe abstract visual reasoning in machine intelligence…
Answer GenerationAttributeOdd One OutVisual ReasoningOne Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems
Abstract Visual Reasoning (AVR) comprises a wide selection of various problems similar to those used in human IQ tests. Recent years have brought dynamic progress in solving particular AVR tasks, however, in the contempo…
Odd One OutTransfer LearningVisual ReasoningCluster Flow: how a hierarchical clustering layer make allows deep-NNs more resilient to hacking, more human-like and easily implements relational reasoning
Despite the huge recent breakthroughs in neural networks (NNs) for artificial intelligence (specifically deep convolutional networks) such NNs do not achieve human-level performance: they can be hacked by images that wou…
Common Sense ReasoningOdd One OutRelational ReasoningSpot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN
This study presents an adversarial method for anomaly detection in real-world applications, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error. Previou…
Anomaly DetectionOdd One OutHuman alignment of neural network representations
Today's computer vision models achieve human or near-human level performance across a wide variety of vision tasks. However, their architectures, data, and learning algorithms differ in numerous ways from those that give…
Odd One OutEvaluating Word Embeddings in Extremely Under-Resourced Languages: A Case Study in Bribri
Word embeddings are critical for numerous NLP tasks but their evaluation in actual under-resourced settings needs further examination. This paper presents a case study in Bribri, a Chibchan language from Costa Rica. Four…
Odd One OutWord EmbeddingsSymmetry as a Representation of Intuitive Geometry?
Recognition of geometrical patterns seems to be an important aspect of human intelligence. Geometric pattern recognition is used in many intelligence tests, including Dehaene's odd-one-out test of Core Geometry (CG)) bas…
Odd One OutVICE: Variational Interpretable Concept Embeddings
A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bay…
Experimental DesignObjectOdd One OutPAC learning+2Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence …
AnachronismsAnalogical SimilarityAnalytic EntailmentCausal Judgment+69Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis o…
Abstract AlgebraAnachronismsAnalogical SimilarityAnalytic Entailment+143Tell me why! Explanations support learning relational and causal structure
Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of explanations--plays a considerable role i…
Odd One OutReinforcement Learning (RL)Tell me why!—Explanations support learning relational and causal structure
Explanations play a considerable role in human learning, especially in areas that remain major challenges for AI—forming abstractions, and learning about the relational and causal structure of the world. Here, we e…
Odd One OutOdd-One-Out Representation Learning
The effective application of representation learning to real-world problems requires both techniques for learning useful representations, and also robust ways to evaluate properties of representations. Recent work in dis…
DisentanglementMetric LearningModel SelectionOdd One Out+2We Have So Much In Common: Modeling Semantic Relational Set Abstractions in Videos
Identifying common patterns among events is a key ability in human and machine perception, as it underlies intelligent decision making. We propose an approach for learning semantic relational set abstractions on videos, …
Decision MakingOdd One OutDo Saliency Models Detect Odd-One-Out Targets? New Datasets and Evaluations
Recent advances in the field of saliency have concentrated on fixation prediction, with benchmarks reaching saturation. However, there is an extensive body of works in psychology and neuroscience that describe aspects of…
Odd One OutEffects of Linguistic Labels on Learned Visual Representations in Convolutional Neural Networks: Labels matter!
We investigated the changes in visual representations learnt by CNNs when using different linguistic labels (e.g., trained with basic-level labels only, superordinate-level only, or both at the same time) and how they co…
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