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Found 14 papers, 7 papers with code
Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models
Motivated by this, we ask the following important question, "How can we leverage the knowledge from a large VFM to train a small task-specific model for a new target task with limited labeled training data?
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training
We further demonstrate the effectiveness of our multi-modal reinforced training by training a CLIP model based on ViT-B/16 image backbone and achieving +2. 9% average performance improvement on 38 evaluation benchmarks compared to the previous best.
TiC-CLIP: Continual Training of CLIP Models
We introduce the first set of web-scale Time-Continual (TiC) benchmarks for training vision-language models: TiC-DataComp, TiC-YFCC, and TiC-Redcaps.
SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding
By applying our method to SAM and CLIP, we obtain SAM-CLIP: a unified model that combines the capabilities of SAM and CLIP into a single vision transformer.
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement
While CLIP is scalable, promptable, and robust to distribution shifts on image classification tasks, it lacks object localization capabilities.
Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals
To achieve effective pretraining in the presence of potential distributional shifts, we propose a frequency-aware masked autoencoder ($\texttt{bio}$FAME) that learns to parameterize the representation of biosignals in the frequency space.
Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement
Models pretrained on ImageNet+ and fine-tuned on CIFAR-100+, Flowers-102+, and Food-101+, reach up to 3. 4% improved accuracy.
FastFill: Efficient Compatible Model Update
The task of retrieving the most similar data from a gallery set to a given query data is performed through a similarity comparison on features.
APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations
Recent advances in learning aligned multimodal representations have been primarily driven by training large neural networks on massive, noisy paired-modality datasets.
Forward Compatible Training for Large-Scale Embedding Retrieval Systems
To avoid the cost of backfilling, BCT modifies training of the new model to make its representations compatible with those of the old model.
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution
We propose extracurricular learning, a novel knowledge distillation method, that bridges this gap by (1) modeling student and teacher output distributions; (2) sampling examples from an approximation to the underlying data distribution; and (3) matching student and teacher output distributions over this extended set including uncertain samples.
Least squares binary quantization of neural networks
We conduct experiments on the ImageNet dataset and show a reduced accuracy gap when using the proposed least squares quantization algorithms.
OPTIMAL BINARY QUANTIZATION FOR DEEP NEURAL NETWORKS
We conduct experiments on the ImageNet dataset and show a reduced accuracy gap when using the proposed optimal quantization algorithms.
Democratizing Production-Scale Distributed Deep Learning
The interest and demand for training deep neural networks have been experiencing rapid growth, spanning a wide range of applications in both academia and industry.
