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Found 21 papers, 7 papers with code
Efficient Bias Mitigation Without Privileged Information
Deep neural networks trained via empirical risk minimisation often exhibit significant performance disparities across groups, particularly when group and task labels are spuriously correlated (e. g., "grassy background" and "cows").
Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation
Despite the growing popularity of methods which learn from unlabeled data, the extent to which these representations rely on spurious features for prediction is unclear.
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
We propose GRACE, a lifelong model editing method, which implements spot-fixes on streaming errors of a deployed model, ensuring minimal impact on unrelated inputs.
Semantic uncertainty intervals for disentangled latent spaces
Meaningful uncertainty quantification in computer vision requires reasoning about semantic information -- say, the hair color of the person in a photo or the location of a car on the street.
Exploring Visual Prompts for Adapting Large-Scale Models
The surprising effectiveness of visual prompting provides a new perspective on adapting pre-trained models in vision.
Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth
In most machine learning tasks unambiguous ground truth labels can easily be acquired.
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators.
MetaReg: Towards Domain Generalization using Meta-Regularization
Training models that generalize to new domains at test time is a problem of fundamental importance in machine learning.
Ranked #68 on
Domain Generalization
on PACS
Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition
We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems.
Improving Network Robustness against Adversarial Attacks with Compact Convolution
In particular, we show that learning features in a closed and bounded space improves the robustness of the network.
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
In this work, we focus on adapting the representations learned by segmentation networks across synthetic and real domains.
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems.
Regularizing deep networks using efficient layerwise adversarial training
In this paper, we present an efficient approach to perform adversarial training by perturbing intermediate layer activations and study the use of such perturbations as a regularizer during training.
Generate To Adapt: Aligning Domains using Generative Adversarial Networks
Domain Adaptation is an actively researched problem in Computer Vision.
Ranked #27 on
Domain Adaptation
on Office-31
Self corrective Perturbations for Semantic Segmentation and Classification
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems.
Deep Convolutional Neural Network Features and the Original Image
The results show that the DCNN features contain surprisingly accurate information about the yaw and pitch of a face, and about whether the face came from a still image or a video frame.
An All-In-One Convolutional Neural Network for Face Analysis
The proposed method employs a multi-task learning framework that regularizes the shared parameters of CNN and builds a synergy among different domains and tasks.
Ranked #9 on
Face Verification
on IJB-A
Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks
Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems.
Triplet Probabilistic Embedding for Face Verification and Clustering
Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem.
Ranked #11 on
Face Verification
on IJB-A
Triplet Similarity Embedding for Face Verification
In this work, we present an unconstrained face verification algorithm and evaluate it on the recently released IJB-A dataset that aims to push the boundaries of face verification methods.
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition
In this paper, we present a brief history of developments in computer vision and artificial neural networks over the last forty years for the problem of image-based recognition.
