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Found 16 papers, 10 papers with code
Learning to Embed Time Series Patches Independently
However, we argue that capturing such patch dependencies might not be an optimal strategy for time series representation learning; rather, learning to embed patches independently results in better time series representations.
Soft Contrastive Learning for Time Series
SoftCLT is a plug-and-play method for time series contrastive learning that improves the quality of learned representations without bells and whistles.
ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers
Combined with a matching loss, it can effectively find objects that are similar to the input patch and complete the missing annotations.
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-training and few-shot learning datasets are from a similar domain.
Improving Transferability of Representations via Augmentation-Aware Self-Supervision
Recent unsupervised representation learning methods have shown to be effective in a range of vision tasks by learning representations invariant to data augmentations such as random cropping and color jittering.
i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
Contrastive representation learning has shown to be effective to learn representations from unlabeled data.
ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds
Our shape-aware adversarial attacks are orthogonal to existing point cloud based attacks and shed light on the vulnerability of 3D deep neural networks.
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dimensional state spaces, such as images.
Robust Determinantal Generative Classifier for Noisy Labels and Adversarial Attacks
For instance, on CIFAR-10 dataset containing 45% noisy training labels, we improve the test accuracy of a deep model optimized by the state-of-the-art noise-handling training method from33. 34% to 43. 02%.
Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild
Lifelong learning with deep neural networks is well-known to suffer from catastrophic forgetting: the performance on previous tasks drastically degrades when learning a new task.
Robust Inference via Generative Classifiers for Handling Noisy Labels
Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such noisy training datasets.
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications.
Ranked #2 on
Out-of-Distribution Detection
on MS-1M vs. IJB-C
Hierarchical Novelty Detection for Visual Object Recognition
The essential ingredients of our methods are confidence-calibrated classifiers, data relabeling, and the leave-one-out strategy for modeling novel classes under the hierarchical taxonomy.
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
The problem of detecting whether a test sample is from in-distribution (i. e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine learning applications.
Towards Understanding the Invertibility of Convolutional Neural Networks
Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible.
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification
Inspired by the recent trend toward revisiting the importance of unsupervised learning, we investigate joint supervised and unsupervised learning in a large-scale setting by augmenting existing neural networks with decoding pathways for reconstruction.
