Search results with tag "Discriminative"
On Discriminative vs. Generative Classifiers: A comparison ...
proceedings.neurips.ccDiscriminant Analysis and logistic regression. Similarly, for the case of discrete inputs it is also well known that the naive Bayes classifier and logistic regression form a Generative-Discriminative pair [4, 5]. To compare generative and discriminative learning, it seems natural to focus on such pairs.
Explainable Artificial Intelligence (XAI)
sites.cc.gatech.edu(discriminative features of the image) with . class definitions (image-independent discriminative features of the class ) A group of people shopping at an outdoor market . There are many vegetables at the fruit stand . Generating Image Captions Generating Visual Explanations Limitations • Limited (indirect at best) explanation of internal logic
Learning Deep Features for Discriminative Localization
arxiv.orgLearning Deep Features for Discriminative Localization Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba Computer Science and Artificial Intelligence Laboratory, MIT fbzhou,khosla,agata,oliva,torralbag@csail.mit.edu Abstract In this work, we revisit the global average pooling layer
Learning Deep Features for Discriminative Localization
cnnlocalization.csail.mit.eduLearning Deep Features for Discriminative Localization Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba Computer Science and Artificial Intelligence Laboratory, MIT {bzhou,khosla,agata,oliva,torralba}@csail.mit.edu Abstract In this work, we revisit the …
Object Detection with Discriminatively Trained Part Based ...
cs.brown.edua discriminative procedure that only requires bounding boxes for the objects in a set of images. The resulting system is both efficient and accurate, achieving state-of- ... [10] or bag-of-features [44]. One of the goals of our work is to address this performance gap.
L2,1-Norm Regularized Discriminative Feature Selection …
www.ijcai.orgrithms, e.g., Fisher score [Duda et al., 2001] , robust regres-sion [Nie et al., 2010], sparse multi-output regression [Zhao et al., 2010] and trace ratio [Nie et al., 2008], usually select featuresaccordingto labels of the training data. Because dis-criminative informationis enclosed in labels, supervised fea-
Functional Analysis of Behavior
storage.outreach.psu.eduAntecedent event: Discriminative stimulus D(S ) Stimulus in whose presence reinforcement is more likely SD present: Sr available SD absent: Sr unavailable Example: Traffic light Stop/go more likely to be reinforced Consequent event: Reinforcement contingency (Sr) If-then relation between a response and a consequence
RBT Initial Competency Assessment Packet: Requirements
www.bacb.comoperations and discriminative stimuli. Differential Reinforcement: Implement differential reinforcement procedures (e.g., DRA, DRO). Extinction: Implement extinction procedures. With a Client Role-Play Professionalism and Requirements Tasks 16-20 Initials Assessment type 16 Session Notes: Generate objective session notes by describing
HOW POWERFUL ARE GRAPH NEURAL NETWORKS
arxiv.orgthe discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test.
Learning RoI Transformer for Oriented Object Detection in ...
openaccess.thecvf.cominstances and enables to better extract discriminative features for object detection. often approached by an oriented and densely packed ob-ject detection task [37, 31, 12], which is new while well-grounded and have attracted much attention in the past decade [27, 30, 26, 18, 1]. Many of recent progress on object detection in aerial im-
Learning Deep Features for Discriminative Localization
openaccess.thecvf.comWeakly-supervised object localization: There have been a number of recent works exploring weakly-supervised object localization using CNNs [1, 16, 2, 15]. Bergamoetal [1]propose atechniqueforself-taughtobject localization involving masking out image regions to iden-tify the regions causing the maximal activations in order to localize objects.
Rectified Linear Units Improve Restricted Boltzmann Machines
www.cs.toronto.eduto improve discriminative performance. Our empirical results in sections 5 and 6 further support this ob-servation. We also give an approximate probabilistic interpretation for the max(0,x) nonlinearity, further justifying their use. 3. Intensity equivariance NReLU’s have some interesting mathematical proper-
SURF: Speeded Up Robust Features - ETH Z
people.ee.ethz.chdiscriminative power. Concerning the photometric deformations, we assume a simple linear model with a scale factor and offset. Notice that our detector and descriptor don’t use colour. The paper is organised as follows. Section 2 describes related work, on which our results are founded. Section 3 describes the interest point detection scheme.
Verbal Behavior Milestones Assessment and Placement ...
storage.outreach.psu.edudiscriminative stimuli Extinction • Reinforcement no longer happens • Behavior fades . 7/28/2016 7 Verbal Behavior ... 4 10 Imitate prosodic features: Syllable stress 5 5 Pitch, loudness, vowel duration Total: 100 EESA: Early Echoic Skills Assessment . 7/28/2016 19
ArcFace: Additive Angular Margin Loss for Deep Face ...
openaccess.thecvf.comobtain highly discriminative features for face recognition. The proposed ArcFace has a clear geometric interpreta-tion due to its exact correspondence to geodesic distance on a hypersphere. We present arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition bench-
Gaussian processes - Stanford University
cs229.stanford.edu1See course lecture notes on “Supervised Learning, Discriminative Algorithms.” 2See course lecture notes on “Regularization and Model Selection.” 3See course lecture notes on “Support Vector Machines.” 4See course lecture notes on “Factor Analysis.” 1
Deep Learning Face Attributes in the Wild
openaccess.thecvf.comDeep Learning Face Attributes in the Wild ... Good features for face localization should be able to capture rich face variations, and more supervised infor-mation on these variations improves the learning process. ... learning of discriminative features. Fig.2 (d) outlines the procedure of attribute recognition. ...
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Discriminative, Discriminant Analysis, Logistic regression, Discriminative features, Learning Deep Features for Discriminative Localization, Object Detection with Discriminatively Trained Part, Features, Norm Regularized Discriminative Feature Selection, Duda, Object localization, Localization, SURF, Gaussian, Deep Learning Face Attributes in the, Learning