Zero Shot And Few Shot Learning Via Embedding Similarity

via “zero-shot and few-shot learning via embedding similarity”

fill-mask model by undefined. 5,92,18,905 downloads.

Unique: Leverages pre-trained bidirectional context to generate semantically rich embeddings that generalize to unseen classes without task-specific fine-tuning; enables rapid prototyping and dynamic category addition

vs others: More practical than true zero-shot methods (e.g., natural language inference) because it uses simple cosine similarity, and more data-efficient than supervised fine-tuning for low-resource scenarios

via “zero-shot image classification via natural language descriptions”

OpenAI's vision-language model for zero-shot classification.

Unique: Uses contrastive pre-training on 400M image-text pairs from the internet to learn a shared embedding space where visual and linguistic concepts align, enabling zero-shot transfer without task-specific fine-tuning. The dual-encoder design (separate image and text pathways) allows flexible composition of new classes at inference time by encoding arbitrary text descriptions.

vs others: Outperforms traditional supervised classifiers on novel categories and requires no labeled training data, whereas models like ResNet-50 require thousands of labeled examples per class and cannot generalize to unseen categories.

via “contrastive language-image embedding generation”

Open reproduction of consastive language-image pretraining (CLIP) and related.

Unique: Provides a fully open-source, reproducible implementation of CLIP with support for multiple vision architectures (ViT, ResNet, ConvNeXt) and text encoders, trained on diverse datasets (LAION, CommonCrawl), enabling researchers to audit training data and fine-tune on custom datasets without proprietary API dependencies

vs others: More flexible and auditable than OpenAI's CLIP API because it's open-source and allows local fine-tuning, but requires more infrastructure setup and computational resources than cloud-based alternatives

via “few-shot learning with in-context example optimization”

GPT-5.4 mini brings the core capabilities of GPT-5.4 to a faster, more efficient model optimized for high-throughput workloads. It supports text and image inputs with strong performance across reasoning, coding,...

Unique: GPT-5.4 Mini uses a learned ranking function to automatically select and order few-shot examples based on relevance to the current task, rather than requiring manual example curation. The model learns which examples are most informative and orders them to create an optimal learning trajectory, improving few-shot performance without additional training.

vs others: More effective few-shot learning than GPT-4 because automatic example ranking adapts to task-specific patterns; faster than full GPT-5.4 through efficient example selection that reduces context window usage while maintaining learning effectiveness.

via “multimodal-few-shot-and-zero-shot-learning”


Unique: Systematically leverages cross-modal alignment to enable more effective few-shot learning, with concrete strategies for using textual descriptions to guide visual learning — a multimodal-specific advantage absent from single-modality few-shot learning

vs others: Unique focus on how multimodal information (visual + textual) enables more effective few-shot learning compared to single-modality meta-learning; integrates prompt-based learning with metric learning approaches

via “zero-shot image classification via text embeddings”

* ⭐ 05/2022: [VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts (VLMo)](https://arxiv.org/abs/2111.02358)

Unique: Leverages the unified embedding space trained with contrastive captioning to enable zero-shot classification without any task-specific adaptation, using the same embeddings that power both image-text retrieval and generation

vs others: Achieves better zero-shot accuracy than CLIP on fine-grained tasks because contrastive captioning training produces richer semantic alignment; more flexible than supervised classifiers but less accurate than fine-tuned models

via “few-shot text classification with minimal training examples”

Unique: Implements few-shot classification by leveraging pre-trained embeddings with lightweight classifiers, avoiding the need for full model retraining or large labeled datasets. This embedding-space classification approach is computationally efficient for Node.js but trades off accuracy potential of full fine-tuning.

vs others: Requires only a few training examples per category versus hundreds needed for traditional supervised learning, making it accessible to teams without ML expertise or large labeled datasets, though accuracy and robustness are likely lower than fine-tuned models.