CLIP Contrastive Learning - Interactive Visualization

CLIP (Contrastive Language–Image Pretraining) trains a joint image-text embedding space by maximizing similarity of matching image-text pairs while minimizing similarity of non-matching pairs. The InfoNCE loss treats each batch of N pairs as N classification problems. This shared space enables zero-shot transfer to any image classification task.

• Batch similarity matrix - see the N×N cosine similarity grid where diagonal entries are positive pairs and off-diagonal are negatives
• InfoNCE loss - watch how the loss pushes matched embeddings together and unmatched embeddings apart simultaneously
• Zero-shot classification - see how text prompts like "a photo of a dog" are compared to image embeddings at inference
• Embedding space visualization - watch image and text embeddings cluster by semantic meaning after contrastive training
• Compare CLIP, ALIGN, and SigLIP loss formulations and understand why SigLIP uses sigmoid instead of softmax

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