Papers Linear-Probe Classification
“Linear-Probe Classification” 태그가 달린 논문 11편 · 필터 해제
Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging
This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised learning in medical imaging, focusing on Alzheimer disease (AD) using b…
AttributeComputational EfficiencyCounterfactual ExplanationData Augmentation+9Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
Latent-based image generative models, such as Latent Diffusion Models (LDMs) and Mask Image Models (MIMs), have achieved notable success in image generation tasks. These models typically leverage reconstructive autoencod…
Conditional Image GenerationImage GenerationLinear-Probe ClassificationSelf-Supervised Image Classification+2SODA: Bottleneck Diffusion Models for Representation Learning
We introduce SODA, a self-supervised diffusion model, designed for representation learning. The model incorporates an image encoder, which distills a source view into a compact representation, that, in turn, guides the g…
DecoderDenoisingImage GenerationLinear-Probe Classification+2Extending global-local view alignment for self-supervised learning with remote sensing imagery
Since large number of high-quality remote sensing images are readily accessible, exploiting the corpus of images with less manual annotation draws increasing attention. Self-supervised models acquire general feature repr…
Change DetectionContrastive LearningImage ClassificationKnowledge Distillation+5Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Vision Transformers (ViT) have introduced the…
Action ClassificationFairnessImage ClassificationLinear-Probe Classification+2Neural Eigenfunctions Are Structured Representation Learners
This paper introduces a structured, adaptive-length deep representation called Neural Eigenmap. Unlike prior spectral methods such as Laplacian Eigenmap that operate in a nonparametric manner, Neural Eigenmap leverages N…
Contrastive LearningData AugmentationFeature ImportanceImage Retrieval+5Text and Code Embeddings by Contrastive Pre-Training
Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, trainin…
Code SearchLinear-Probe ClassificationNatural QuestionsPassage Ranking+3SimCSE: Simple Contrastive Learning of Sentence Embeddings
This paper presents SimCSE, a simple contrastive learning framework that greatly advances state-of-the-art sentence embeddings. We first describe an unsupervised approach, which takes an input sentence and predicts itsel…
Contrastive LearningData AugmentationLinear-Probe ClassificationNatural Language Inference+4DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various down…
ClusteringContrastive LearningLinear-Probe ClassificationMetric Learning+4Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are …
ClusteringLinear-Probe ClassificationSemantic SimilaritySemantic Textual Similarity+6BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bid…
Citation Intent ClassificationCommon Sense ReasoningConversational Response SelectionCoreference Resolution+17