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End-to-End Incremental Learning

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Attentional Feature Fusion

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Training Generative Adversarial Networks with Limited Data

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Evaluating the Visualization of What a Deep Neural Network Has Learned

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Equivalent-accuracy accelerated neural-network training using analogue memory

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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

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Benchmarking Deep Reinforcement Learning for Continuous Control

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AutoAugment: Learning Augmentation Policies from Data

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Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

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Federated Learning via Over-the-Air Computation

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DINOv2: Learning Robust Visual Features without Supervision

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Vision-Language Models for Vision Tasks: A Survey

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Federated learning on non-IID data: A survey

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Big Transfer (BiT): General Visual Representation Learning

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Big Transfer (BiT): General Visual Representation Learning

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CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models

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CMT: Convolutional Neural Networks Meet Vision Transformers

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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

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Zero-Shot Learning Through Cross-Modal Transfer

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Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings

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Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

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Importance Estimation for Neural Network Pruning

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Stand-Alone Self-Attention in Vision Models

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Adversarial Examples Are Not Bugs, They Are Features

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A survey of unsupervised deep domain adaptation

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Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

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Decoupled Knowledge Distillation

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Symmetric Cross Entropy for Robust Learning With Noisy Labels

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EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention

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Client-Edge-Cloud Hierarchical Federated Learning

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HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition

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Deep Supervised Hashing for Fast Image Retrieval

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Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

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Data Poisoning Attacks Against Federated Learning Systems

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Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization

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Active Learning for Convolutional Neural Networks: A Core-Set Approach

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Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

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FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

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An Analysis of Scale Invariance in Object Detection - SNIP

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CondenseNet: An Efficient DenseNet Using Learned Group Convolutions

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Data augmentation: A comprehensive survey of modern approaches

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Trained Ternary Quantization

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Scaling Vision Transformers

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Adaptive Federated Optimization

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ResMLP: Feedforward networks for image classification with data-efficient training

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ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training

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Joint Unsupervised Learning of Deep Representations and Image Clusters

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A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

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Confident Learning: Estimating Uncertainty in Dataset Labels

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Do ImageNet Classifiers Generalize to ImageNet?

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CNN: Single-label to Multi-label

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Realistic Evaluation of Deep Semi-Supervised Learning Algorithms

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Deep neural network models for computational histopathology: A survey

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Interpretation of Neural Networks Is Fragile

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Learning Loss for Active Learning

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Density estimation using Real NVP

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Certified Robustness to Adversarial Examples with Differential Privacy

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SNAS: Stochastic Neural Architecture Search

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An empirical survey of data augmentation for time series classification with neural networks

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Contrastive Clustering

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Machine Unlearning

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A Comprehensive Overhaul of Feature Distillation

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Residual Networks Behave Like Ensembles of Relatively Shallow Networks

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Deep Metric Learning: A Survey

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This Looks Like That: Deep Learning for Interpretable Image Recognition

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Deep Pyramidal Residual Networks

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Label Propagation for Deep Semi-Supervised Learning

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The Power of Ensembles for Active Learning in Image Classification

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HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

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Backdoor Learning: A Survey

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Zero-Shot Learning via Semantic Similarity Embedding

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Incorporating Convolution Designs into Visual Transformers

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Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges

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The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

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Deep hashing for compact binary codes learning

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A review of object detection based on deep learning

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Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning

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Deep double descent: where bigger models and more data hurt*

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PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

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Revisiting Knowledge Distillation via Label Smoothing Regularization

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Potential, challenges and future directions for deep learning in prognostics and health management applications

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Binary Neural Networks: A Survey

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Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network

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Mish: A Self Regularized Non-Monotonic Activation Function

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A comprehensive review of object detection with deep learning

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The Computational Limits of Deep Learning

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Generalized Out-of-Distribution Detection: A Survey

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Machine Learning Models that Remember Too Much

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Global Second-Order Pooling Convolutional Networks

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Few-Shot Class-Incremental Learning

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