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Found 11 papers, 3 papers with code
Local Statistics for Generative Image Detection
Diffusion models (DMs) are generative models that learn to synthesize images from Gaussian noise.
Mugs: A Multi-Granular Self-Supervised Learning Framework
It provides complementary instance supervision to IDS via an extra alignment on local neighbors, and scatters different local-groups separately to increase discriminability.
Ranked #13 on
Self-Supervised Image Classification
on ImageNet
Contrastive Learning
Self-Supervised Image Classification
+3
Adaptive Modeling Against Adversarial Attacks
Adversarial training, the process of training a deep learning model with adversarial data, is one of the most successful adversarial defense methods for deep learning models.
SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis
Specifically, in terms of usability, it is demanding for non-experts to implement deep learning models, obtain the right settings for the entire machine learning pipeline, manage models and datasets, and exploit external data sources all together.
Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier
Furthermore, on the adversarial counterparts, with the image transformation, the resulting shapes of the distribution of softmax are similar to the distributions from the clean images.
Enhancing Transformation-based Defenses using a Distribution Classifier
Furthermore, on the adversarial counterparts, with the image transformation, the resulting shapes of the distribution of softmax are similar to the distributions from the clean images.
Theoretical and Experimental Analysis on the Generalizability of Distribution Regression Network
However, in Kou et al. (2018) and some other works on distribution regression, there is a lack of comprehensive comparative study on both theoretical basis and generalization abilities of the methods.
PANDA: Facilitating Usable AI Development
In many complex applications such as healthcare, subject matter experts (e. g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results.
Rafiki: Machine Learning as an Analytics Service System
Second, expertise knowledge is required to optimize the training and inference procedures in terms of efficiency and effectiveness, which imposes heavy burden on the system users.
A Compact Network Learning Model for Distribution Regression
Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces.
Distribution Regression Network
We introduce our Distribution Regression Network (DRN) which performs regression from input probability distributions to output probability distributions.
