Deep Active Learning in the Presence of Label Noise: A Survey

Deep active learning has emerged as a powerful tool for training deep learning models within a predefined labeling budget. These models have achieved performances comparable to those trained in an offline setting. However, deep active learning faces substantial issues when dealing with classification datasets containing noisy labels. In this literature review, we discuss the current state of deep active learning in the presence of label noise, highlighting unique approaches, their strengths, and weaknesses. With the recent success of vision transformers in image classification tasks, we provide a brief overview and consider how the transformer layers and attention mechanisms can be used to enhance diversity, importance, and uncertainty-based selection in queries sent to an oracle for labeling. We further propose exploring contrastive learning methods to derive good image representations that can aid in selecting high-value samples for labeling in an active learning setting. We also highlight the need for creating unified benchmarks and standardized datasets for deep active learning in the presence of label noise for image classification to promote the reproducibility of research. The review concludes by suggesting avenues for future research in this area.Comment: 20 pages, PhD literature revie

Algorithmic implementation of expert object recognition in ventral visual pathway, An

2002 Fall.Includes bibliographical references (pages 148-160) and index.Understanding the mechanisms underlying visual object recognition has been an important subject in both human and machine vision since the early days of cognitive science. Current state-of-the-art machine vision systems can perform only rudimentary tasks in highly constrained situations compared to the powerful and flexible recognition abilities of the human visual system. In this work, we provide an algorithmic analysis of psychological and anatomical models of the ventral visual pathway, more specifically the pathway that is responsible for expert object recognition, using the current state of machine vision technology. As a result, we propose a biologically plausible expert object recognition system composed of a set of distinct component subsystems performing feature extraction and pattern matching. The proposed system is evaluated on four different multi-class data sets, comparing the performance of the system as a whole to the performance of its component subsystems alone. The results show that the system matches the performance of state-of-the-art machine vision techniques on uncompressed data, and performs better when the stored data is highly compressed. Our work on building an artificial vision system based on biological models and theories not only provides a baseline for building more complex, end-to-end vision systems, but also facilitates interactions between computational and biological vision studies by providing feedback to both communities

2004 Special Issue Integration of form and motion within a generative model of visual cortex

One of the challenges faced by the visual system is integrating cues within and across processing streams for inferring scene properties and structure. This is particularly apparent in the inference of object motion, where psychophysical experiments have shown that integration of motion signals, distributed across space, must also be integrated with form cues. This has led several to conclude that there exist mechanisms which enable form cues to ‘veto ’ or completely suppress ambiguous motion signals. We describe a probabilistic approach which uses a generative network model for integrating form and motion cues using the machinery of belief propagation and Bayesian inference. We show, using computer simulations, that motion integration can be mediated via a local, probabilistic representation of contour ownership, which we have previously termed ‘direction of figure’. The uncertainty of this inferred form cue is used to modulate the covariance matrix of network nodes representing local motion estimates in the motion stream. We show with results for two sets of stimuli that the model does not completely suppress ambiguous cues, but instead integrates them in a way that is a function of their underlying uncertainty. The result is that the model can account for the continuum of bias seen for motion coherence and perceived object motion in psychophysical experiments

Implementing the Expert Object Recognition Pathway

This paper presents a four-stage functional model of the expert object recognition pathway, where each stage models one area of anatomic activation. It implements this model in an end-to-end computer vision system, and tests it on real images to provide feedback for the cognitive science and computer vision communitie

Factor Analysis for Background Suppression

Factor analysis (FA) is a statistical technique similar to principal component analysis (PCA) for explaining the variance in a data set in terms of underlying linear factors. Unlike PCA, however, FA has not been widely exploited for face or object recognition. This paper explains the differences between PCA and FA, and confirms that PCA outperforms FA in a standard face recognition task. However, because FA estimates the unique variance independently for every pixel, we show that the variance estimates from FA can be used to automatically detect and suppress background pixels prior to the application of PCA, and thereby improve the performance of PCA-based object recognition systems

Prediction of Mature MicroRNA and Piwi-Interacting RNA without a Genome Reference or Precursors

The discovery of novel microRNA (miRNA) and piwi-interacting RNA (piRNA) is an important task for the understanding of many biological processes. Most of the available miRNA and piRNA identification methods are dependent on the availability of the organism’s genome sequence and the quality of its annotation. Therefore, an efficient prediction method based solely on the short RNA reads and requiring no genomic information is highly desirable. In this study, we propose an approach that relies primarily on the nucleotide composition of the read and does not require reference genomes of related species for prediction. Using an empirical Bayesian kernel method and the error correcting output codes framework, compact models suitable for large-scale analyses are built on databases of known mature miRNAs and piRNAs. We found that the usage of an L1-based Gaussian kernel can double the true positive rate compared to the standard L2-based Gaussian kernel. Our approach can increase the true positive rate by at most 60% compared to the existing piRNA predictor based on the analysis of a hold-out test set. Using experimental data, we also show that our approach can detect about an order of magnitude or more known miRNAs than the mature miRNA predictor, miRPlex

Adore: Adaptive object recognition

Abstract. Many modern computer vision systems are built by chaining together standard vision procedures, often in graphical programming environments such as Khoros, CVIPtools or IUE. Typically, these procedures are selected and sequenced by an ad-hoc combination of programmer’s intuition and trial-and-error. This paper presents a theoretically sound method for constructing object recognition strategies by casting object recognition as a Markov Decision Problem (MDP). The result is a system called ADORE (Adaptive Object Recognition) that automatically learns object recognition control policies from training data. Experimental results are presented in which ADORE is trained to recognize five types of houses in aerial images, and where its performance can be (and is) compared to optimal.